HDATF
English
Start a conversation
Menu

Paper Archive

Every archived paper with its summary, highest number first.

Papers 1,193Archive updated BenchmarksRSS feed
  1. 094Search Agents / EfficiencyarXiv:2511.17006

    Budget-Aware Tool-Use Enables Effective Agent Scaling

    • Standard tool-augmented LLM agents lack intrinsic budget awareness, so they use resources inefficiently and quickly hit performance limits even when the tool-use budget increases.
    • There was no systematic study or formal cost metric for jointly evaluating LLM token consumption and the economic cost of external tool interactions during agent test-time scaling.
    • Agents often explore too shallowly or stop early without strategically using the extra resources they have available, which leads to suboptimal results and wasted compute.
    • The authors developed Budget Tracker, a lightweight plug-and-play module that provides agents with real-time, continuous budget awareness through prompt-level policy guidance and state updates.
    • They introduced BATS, or Budget Aware Test-time Scaling, a training-free framework that dynamically adjusts planning, including constraint decomposition and structured dynamic planning, and self-verification strategies using continuous budget awareness.
    • They formalized a unified cost metric that comprehensively covers both internal LLM token consumption and the economic cost of external tool interactions, making scaling efficiency easier to evaluate transparently.
    AbstractPDF
  2. 093Agents / TheoryarXiv:2512.10047

    Detailed balance in large language model-driven agents

    • There is no unified theoretical framework for understanding the emergent behavior of large language model (LLM)-based agents at the macroscopic level.
    • The complex internal engineering of LLM agents often produces black-box behavior, making their dynamics hard to predict or analyze.
    • Current theoretical understanding of LLMs focuses mainly on microscopic token-level properties and does not explain emergent goal-directed agent behavior.
    • We formalize the generation process of LLMs inside agents as a Markov transition process over a defined state space.
    • We hypothesize an implicit potential function (VT) for LLMs and estimate it quantitatively using a minimum-action principle.
    • Experimental validation includes measuring transition probabilities between states generated by LLMs and statistically confirming detailed balance conditions across tasks and models.
    AbstractPDF
  3. 092Math Reasoning / BenchmarksarXiv:2511.01846

    Towards Robust Mathematical Reasoning

    • Existing mathematical reasoning benchmarks, such as GSM8K, MATH, and AIME, are approaching saturation, which limits their usefulness for distinguishing advanced model capability.
    • Many current benchmarks rely mainly on final-answer matching, which can let models guess or memorize answers without demonstrating robust multi-step reasoning.
    • There is a lack of a comprehensive evaluation framework for assessing deeper mathematical understanding, such as the ability to generate and rigorously evaluate proofs.
    • The paper introduces IMO-Bench, a comprehensive collection of three benchmarks: IMO-AnswerBench for robust problem solving, IMO-Proof Bench for rigorous proof writing, and IMO-GradingBench for proof evaluation.
    • IMO-AnswerBench uses 400 olympiad problems across four categories and applies explicit hardening techniques, such as paraphrasing and numerical changes, to prevent data memorization.
    • IMO-Proof Bench provides 60 IMO-level proof problems split into foundational and advanced sets, and it is evaluated mainly by human experts on a 0 to 7 scale that mimics traditional IMO grading.
    AbstractPDF
  4. 091Research AgentsarXiv:2510.17797

    Enterprise Deep Research: Steerable Multi-Agent Deep Research for Enterprise Analytics

    • Current autonomous agents lack domain specialization and seamless integration, making it difficult for enterprises to turn exponentially growing unstructured data into actionable insights.
    • Existing deep research systems are often opaque black boxes, lacking transparency, auditability, and real-time controllability, which makes them a poor fit for dynamic enterprise environments.
    • Most current systems are designed for open-domain web data and do not adequately handle heterogeneous, proprietary internal enterprise sources such as databases and files.
    • A modular multi-agent architecture coordinates a master research agent, specialized retrieval agents for the web, academic sources, GitHub, and LinkedIn, a visualization agent, and a Reflection mechanism for adaptive query decomposition and execution.
    • Through a Research Todo Manager that maintains a human-readable todo.md file, it introduces steerable context engineering, allowing real-time natural-language human intervention to dynamically adjust agent plans and priorities.
    • It uses an extensible Model Context Protocol (MCP)-based tool ecosystem to integrate domain-specific tools such as file analysis and NL2SQL agents, enabling reasoning over diverse proprietary enterprise data sources.
    AbstractPDF
  5. 090LLMs / ReasoningarXiv:2510.18855

    Every Step Evolves: Scaling Reinforcement Learning for Trillion-Scale Thinking Model

    • The lack of an open-source one-trillion-parameter thinking model has made advanced reasoning capabilities less accessible to the broader AI research community.
    • Scaling reinforcement learning (RL) to a one-trillion-parameter Mixture-of-Experts (MoE) model led to substantial instability caused by training-inference mismatch.
    • Inefficiencies in handling long reasoning trajectories and system bottlenecks in RL infrastructure created major challenges at an unprecedented scale.
    • They developed Ring-1T, a one-trillion-parameter MoE model trained through a multi-stage pipeline that includes Long Chain-of-Thought supervised fine-tuning and large-scale reinforcement learning.
    • They introduced IcePop, an RL algorithm variant that stabilizes training by suppressing noisy gradient updates through bilateral masking correction for probability drift.
    • They designed C3PO++ for efficient trajectory processing with dynamic, budget-controlled partitioning, and they built ASystem, a high-performance distributed RL infrastructure tuned for the trillion-parameter scale.
    AbstractPDF
  6. 089Reasoning / InferencearXiv:2510.14901

    Reasoning with Sampling: Your Base Model is Smarter Than You Think

    • It is unclear whether RL fundamentally teaches new reasoning capabilities to LLMs or simply sharpens already latent capabilities.
    • Current RL-based LLM post-training methods inherently require high compute cost, curated datasets, and external verifiers.
    • RL-post-trained models often suffer from diversity collapse, sacrificing multi-sample reasoning performance, pass@k, in favor of single-sample gains.
    • The base LLM's power distribution is defined by sampling from the original distribution raised to exponent α (α >= 1), which mathematically reweights the original distribution to favor higher-likelihood sequences.
    • The Metropolis-Hastings (MH) algorithm, a Markov chain Monte Carlo (MCMC) method, is used to enable approximate sampling from the unnormalized power distribution.
    • An iterative, blockwise autoregressive MCMC approach is introduced that progressively refines sequence segments, managing complexity and improving mixing time for long-sequence generation.
    AbstractPDF
  7. 088Continual LearningarXiv:2510.15103

    Continual Learning via Sparse Memory Finetuning

    • Large language models suffer from catastrophic forgetting, meaning they lose previously learned knowledge when updated with new information.
    • Current continual-learning methods for LLMs, such as replay-based and regularization-based approaches, are often compute-heavy, data-inefficient, or require trade-offs between new learning and retaining old knowledge.
    • LLMs are usually deployed as static models after training, which limits their ability to adapt to real-time information, user feedback, and changing world knowledge.
    • This paper proposes sparse memory fine-tuning, which uses a memory hierarchy where each token accesses only a small portion of a large memory pool.
    • It applies TF-IDF-like ranking scores to identify the most knowledge-specific memory indices in a new training batch, reducing interference with general knowledge.
    • During fine-tuning, only the top t memory indices identified by TF-IDF scores are updated, while all other memory parameters and the base LLM parameters remain fixed through gradient masking.
    AbstractPDF
  8. 087Vision / CompressionarXiv:2510.18234

    DeepSeek-OCR: Contexts Optical Compression

    • As input sequence length increases, LLMs face major compute and memory challenges because computation grows quadratically, limiting their ability to handle long text contexts.
    • Existing vision encoders in vision-language models (VLMs) suffer from limitations such as complex deployment, excessive image splitting, and high activation memory consumption for high-resolution documents.
    • Traditional OCR models did not explicitly address the optimal vision-text compression ratio or the minimum number of vision tokens needed for accurate text decoding when integrated with LLMs for long-context understanding.
    • DeepSeek-OCR is an end-to-end vision-language model composed of a new 380M-parameter DeepEncoder and a DeepSeek3B-MoE decoder.
    • DeepEncoder combines SAM-base and CLIP-large with a core 16x token compressor, a two-layer convolutional neural network, to efficiently process high-resolution images, reduce activation memory, and produce only a small number of vision tokens.
    • The model supports multiple resolution modes, including a dynamic "Gundam" mode that handles ultra-high-resolution inputs through tiling, and is trained on diverse datasets spanning traditional OCR, complex synthetic images such as charts, equations, and diagrams, and general vision data.
    AbstractPDF
  9. 086Benchmarks / AgentsarXiv:2508.15760

    LiveMCP-101: Stress Testing and Diagnosing MCP-enabled Agents on Challenging Queries

    • AI agents show promising prototype performance, but they lack reliability and robustness when deployed in real, dynamic operating environments.
    • Existing benchmarks for tool-augmented LLMs are limited because they evaluate only single-step tool calls, operate in synthetic environments, or focus on low task complexity, so they miss the complexity of real tool interaction.
    • There has been no clear framework for diagnosing the concrete failure modes of agents that handle diverse, time-varying external tools.
    • We constructed LiveMCP-101, a benchmark of 101 carefully selected real-world queries that require multi-step coordination across diverse MCP-enabled tools.
    • We propose a new evaluation methodology that uses gold execution plans and parallel live execution to reflect the dynamic and evolving nature of live MCP tool responses.
    • We use a human-expert-validated LLM-as-a-judge approach to provide fine-grained scoring for both final outputs and the agent's execution trajectory.
    AbstractPDF
  10. 085Reasoning / RL / SurveyarXiv:2509.08827

    A Survey of Reinforcement Learning for Large Reasoning Models

    • The rapid success of RL in transforming LLMs into LRMs faces substantial challenges related to compute, optimal algorithm design, and scalable high-quality training data.
    • Existing RL applications for LLMs have focused mainly on human alignment, overlooking RL's potential to fundamentally improve complex reasoning abilities.
    • The lack of a unified framework for understanding and navigating the complex, fast-moving landscape of RL for reasoning models hinders systematic research and development.
    • The authors conduct a comprehensive literature review and organize the field into its foundational components: reward design, policy optimization, and sampling strategies.
    • The survey introduces and highlights reinforcement learning with verifiable rewards, or RLVR, as a key paradigm for strengthening reasoning on tasks with objective feedback.
    • It clarifies and discusses foundational questions and ongoing debates, such as RL's role in sharpening versus discovering reasoning and the interaction between RL and supervised fine-tuning, or SFT.
    AbstractPDF
  11. 084Multi-Agent / DebatearXiv:2509.05396

    Talk Isn't Always Cheap: Understanding Failure Modes in Multi-Agent Debate

    • There is a common belief that multi-agent debate universally improves large language model performance on complex reasoning tasks.
    • There is still a limited understanding of the specific failure modes that arise when multiple LLMs interact, especially in heterogeneous groups with varied capabilities.
    • It is necessary to clarify when and why multi-agent debate can be harmful rather than beneficial.
    • The authors used a standard multi-agent debate framework in which LLMs iteratively revise their responses over multiple rounds based on peer input.
    • They conducted an empirical study with heterogeneous groups of three LLMs, GPT-4o-mini, LLaMA-3.1-8B-Instruct, and Mistral-7B-Instruct-v0.2, across three datasets, CommonSenseQA, MMLU, and GSM8K.
    • They analyzed failure patterns through sequential revision traces, social influence evaluation using the probability of answer flipping after peer agreement, and interventions designed to reduce sycophancy.
    AbstractPDF
  12. 083Agents / RLarXiv:2509.08755

    AgentGym-RL: Training LLM Agents for Long-Horizon Decision Making through Multi-Turn Reinforcement Learning

    • There is no integrated, interactive reinforcement learning (RL) framework that can train LLM agents from scratch for complex, multi-turn, long-horizon tasks.
    • Existing methods for developing LLM agents, such as prompting and supervised fine-tuning (SFT), often rely on proprietary models or human-curated data, limiting scalability, adaptability, and intrinsic self-improvement.
    • Traditional RL applications to LLMs are mostly limited to single-turn tasks or face instability and inefficiency when extended to multi-turn agentic decision making.
    • AgentGym-RL is a modular open-source framework that separates the environment, agent, and training modules to support diverse multi-turn long-horizon tasks.
    • ScalingInter-RL is a new progressive interactive scaling strategy that adaptively increases the maximum number of turns during RL training to balance exploration and exploitation while improving optimization stability and efficiency.
    • The framework integrates a full RL pipeline that supports mainstream online RL algorithms such as PPO and GRPO, with engineering optimizations for scalability and reliability across a variety of realistic environments.
    AbstractPDF
  13. 082Reasoning / ParallelismarXiv:2509.04475

    ParaThinker: Native Parallel Thinking as a New Paradigm to Scale LLM Test-time Compute

    • Sequential LLM reasoning shows tunnel vision, where early mistakes cause diminishing returns and stagnation even as compute increases.
    • Current depth-scaling approaches become inefficient beyond a certain point and fail to unlock further gains on complex reasoning tasks.
    • Existing parallel or search-based methods often depend on external verifiers or domain-specific components, or they add substantial extra compute.
    • ParaThinker uses a two-stage process: parallel reasoning that generates multiple independent thought paths, followed by summarization that integrates those paths into a single final answer.
    • It uses special control tokens, `think i` and `summary`, to guide the model in generating diverse reasoning paths and then merging them.
    • Thought-specific positional embeddings augment standard RoPE to resolve positional ambiguity across parallel paths, ensuring distinct processing and effective summarization.
    AbstractPDF
  14. 081RL / WritingarXiv:2509.04903

    ACE-RL: Adaptive Constraint-Enhanced Reward for Long-form Generation Reinforcement Learning

    • Existing supervised fine-tuning approaches for long-form generation rely heavily on high-quality datasets that are scarce and expensive.
    • Current reinforcement learning, or RL, methods use coarse and subjective evaluation metrics, which fail to capture the subtle and instruction-specific requirements that matter for high-quality long-form writing.
    • LLMs struggle to consistently satisfy fine-grained, multi-faceted, and instruction-specific requirements in complex long-form generation tasks.
    • An automated pipeline using a strong LLM decomposes high-level user instructions into a verifiable checklist of constraints covering content completeness, structural logic, and stylistic formality.
    • The new reward mechanism combines length rewards with fine-grained constraint rewards, and a smaller verifier LLM evaluates whether each constraint is satisfied with a three-level judgment.
    • Training uses the Group Relative Policy Optimization, or GRPO, algorithm, which encourages the LLM to produce long-form responses that satisfy both length and instruction-specific requirements through this comprehensive reward.
    AbstractPDF
  15. 080RAG / EfficiencyarXiv:2509.01092

    REFRAG: Rethinking RAG based Decoding

    • Retrieval-augmented generation (RAG) systems that use large language models (LLMs) suffer from substantial system latency and high memory consumption when processing long retrieved contexts.
    • This creates a tradeoff in which enriching knowledge with longer contexts often reduces system efficiency and throughput.
    • General-purpose optimization for long-context reasoning in LLMs does not properly handle the RAG-specific block-diagonal attention pattern and redundant computation inherent in RAG contexts.
    • A framework using a lightweight encoder compresses RAG contexts into chunk embeddings, greatly reducing the effective input sequence length for a decoder-only LLM.
    • The encoder and decoder are aligned through a continual pretraining (CPT) scheme that includes next-paragraph prediction and reconstruction tasks, with curriculum learning used to manage complexity.
    • A reinforcement-learning policy dynamically expands important context chunks into full token sequences and compresses less important chunks, enabling flexible compression at arbitrary positions.
    AbstractPDF
  16. 079Reasoning / RLarXiv:2509.03646

    Emergent Hierarchical Reasoning in LLMs through Reinforcement Learning

    • Despite empirical success, the mechanistic understanding of how reinforcement learning (RL) improves complex reasoning in LLMs remained unclear.
    • Puzzling phenomena during RL training, such as the aha moment and length-scaling effects, lacked a unified explanation.
    • Existing RL algorithms often apply optimization pressure indiscriminately to every generated token, which leads to inefficient learning.
    • We functionally decomposed LLM-generated tokens into high-level planning tokens, such as logical maneuvers, and low-level execution tokens, such as calculations.
    • We introduced Strategic Grams (SGs), a data-driven method that empirically identifies planning tokens without subjective manual annotation.
    • We developed Hierarchy-Aware Credit Assignment (HICRA), an RL algorithm that concentrates optimization by amplifying advantage signals on identified planning tokens.
    AbstractPDF
  17. 078Research Agents / RLarXiv:2509.06283

    SFR-DeepResearch: Towards Effective Reinforcement Learning for Autonomously Reasoning Single Agents

    • It equips large language models, or LLMs, with advanced reasoning and tool-use capabilities so they can carry out complex, long-horizon tasks while mitigating issues such as hallucination.
    • It develops an effective autonomous single-agent system for deep research that works without rigid multi-agent orchestration and generalizes better.
    • It stabilizes reinforcement learning, or RL, for multi-turn agentic tasks, where existing methods struggle with stability and can degrade the base model's reasoning ability.
    • It develops an agentic reasoning scaffold for a specific LLM family with the minimal set of three tools, Internet search, page viewing, and a code interpreter, and an iterative single-turn workflow.
    • It builds a new synthetic data generation pipeline that produces difficult multi-hop QA and long-form report-writing tasks for agentic training.
    • It implements an end-to-end RL recipe with a length-normalized REINFORCE objective, trajectory filtering, and partial rollouts to ensure stable policy optimization over multi-turn interactions.
    AbstractPDF
  18. 077Medical AI / RAGarXiv:2508.11386

    Retrieval-augmented reasoning with lean language models

    • State-of-the-art retrieval-augmented generation (RAG) and reasoning systems depend on large external language models, which introduce privacy and security risks in applications that handle sensitive data.
    • Deploying and running large frontier language models locally is computationally burdensome for many organizations because of their high resource demands.
    • General-purpose language models often lack deep, verifiable expertise in specialized domains, leading to inaccuracy or hallucination in critical applications.
    • The researchers used Qwen2.5-Instruct models ranging from 1.5B to 32B parameters as an efficient language-model backbone for reasoning and generation.
    • They developed a retrieval system using semantic embeddings, a vector database, and a new strategy that retrieves whole documents and summarizes them in advance to manage context length while preserving relevant information.
    • To supervise supervised fine-tuning of the lightweight language models, they generated synthetic domain-specific data with diverse user queries and detailed reasoning trajectories using high-performance models (GPT-4o and DeepSeek-R1).
    AbstractPDF
  19. 076Computer Use AgentsarXiv:2508.09123

    OpenCUA: Open Foundations for Computer-Use Agents

    • Most state-of-the-art computer-use agent, or CUA, systems are proprietary and closed-source, which makes it harder to study their capabilities, limitations, and risks.
    • There is no scalable open-source infrastructure for collecting diverse, large-scale computer-use data, including real-time user interaction and accessibility trees.
    • Existing open-source CUA datasets have limited scope, scale, and diversity, and they focus on narrow domains rather than general computer-use applications.
    • The authors developed AGENTNET TOOL, a cross-platform application that captures human computer-use demonstrations, including screen video, mouse and keyboard signals, and accessibility trees, across Windows, macOS, and Ubuntu.
    • They built AGENTNET, the first large-scale and diverse computer-use task dataset with 22,625 trajectories spanning multiple operating systems and more than 200 applications and websites.
    • To improve agent planning and error recovery, they propose a scalable training pipeline that integrates a new reflective long-chain-of-thought synthesis and multi-image history context encoding.
    AbstractPDF
  20. 075Computer Use AgentsarXiv:2508.14040

    ComputerRL: Scaling End-to-End Online Reinforcement Learning for Computer Use Agents

    • The human-computer interface is inherently designed for people, which makes robust LLM-based GUI agents difficult to build because machine interaction is more complex.
    • Existing approaches such as behavior cloning suffer from scalability limits, weak generalization, and poor error recovery on diverse and complex desktop tasks.
    • Applying reinforcement learning to desktop automation has been constrained by heavy compute demands, slow convergence, instability, and entropy collapse, where policies lose exploration ability during long training.
    • The API-GUI interaction paradigm integrates direct GUI interaction with programmatic API calls, and it is complemented by an LLM-driven automation workflow for API development to improve efficiency and versatility.
    • To collect data efficiently, the team developed a scalable and reliable Ubuntu environment with distributed multi-node clustering infrastructure that can orchestrate thousands of concurrent virtual desktop environments.
    • They implemented a fully asynchronous RL framework with multi-LLM behavior cloning, step-level Group Relative Policy Optimization (GRPO) with verifiable rewards, and an Entropulse strategy for continued exploration through periodic RL-SFT alternation.
    AbstractPDF
  21. 074Agents / DistillationarXiv:2508.13167

    Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL

    • Existing multi-agent systems, or MAS, suffer from high computational overhead, generalization issues, and a lack of end-to-end data-driven training because LLM backbones are not inherently trained for multi-turn multi-agent workflows.
    • Existing tool-integrated reasoning, or TIR, models are constrained by rigid think-act-observe workflows and do not natively support flexible multi-agent collaboration patterns.
    • Both MAS and TIR paradigms fail to make effective use of end-to-end training to learn complex, dynamic multi-agent, multi-tool orchestration within a single unified LLM.
    • The Chain-of-Agents, or CoA, paradigm integrates multi-agent collaboration inside the decoding process of a single LLM, dynamically activating role agents such as thinking, planning, and reflection as well as tool agents such as search, crawling, and code generation.
    • An agentic supervised fine-tuning, or SFT, framework distills expert-level multi-agent trajectories into CoA-compatible training data and uses progressive quality filtering to ensure high-quality, complex problem-solving patterns.
    • Agentic reinforcement learning, or RL, further refines the LLM's multi-tool orchestration policy through outcome-based rewards, strengthening strategic tool use and adaptive reasoning on demanding task-specific queries.
    AbstractPDF
  22. 073Routing / EfficiencyarXiv:2508.12631

    Beyond GPT-5: Making LLMs Cheaper and Better via Performance-Efficiency Optimized Routing

    • Balancing high performance, including accuracy and reasoning, with computational efficiency, including cost and latency, is a core challenge in advancing large language models.
    • State-of-the-art proprietary LLMs often come with substantial operating costs and resource demands, which limit broad and cost-effective deployment.
    • Existing test-time routing methods for LLMs often focus mainly on aggregate performance or add overhead by requiring complex training of additional neural networks.
    • The paper develops Avengers-Pro, a test-time routing framework that coordinates diverse LLM ensembles to dynamically select the best model for each query.
    • The framework relies on three lightweight unsupervised operations: query embedding, k-means clustering of queries, and per-cluster model-level performance-efficiency scoring.
    • During online inference, it embeds incoming queries, assigns them to the nearest cluster, and routes them to the model with the highest aggregate performance-efficiency score, adjustable by the tradeoff parameter α.
    AbstractPDF
  23. 072Vision / ReasoningarXiv:2506.22146

    Visual Structures Helps Visual Reasoning: Addressing the Binding Problem in VLMs

    • Large vision-language models, or LVLMs, struggle with precise visual understanding such as exact counting, spatial reasoning, and detailed scene description.
    • LVLMs suffer from the binding problem, which prevents them from reliably connecting perceptual features to the correct visual objects and spatial properties in complex scenes.
    • Existing LVLMs often process visual features in parallel, which causes interference and misattribution errors, especially in cluttered environments.
    • The VISER method adds evenly spaced horizontal lines to the input image so that the image is divided into separate visual compartments or rows.
    • It prepends a short text instruction such as 'Scan the image sequentially along the horizontal lines present in the image' to the original prompt so that the LVLM processes the content region by region.
    • This lightweight, model-agnostic approach introduces only negligible compute overhead and requires no architectural changes, fine-tuning, or multi-query inference.
    AbstractPDF
  24. 071Reasoning / DataarXiv:2507.01921

    NaturalThoughts: Selecting and Distilling Reasoning Traces for General Reasoning Tasks

    • There was no systematic understanding of which characteristics of reasoning demonstrations are most effective for distilling reasoning ability into smaller language models.
    • The prevailing Less is More hypothesis, which claims that a few handpicked examples are enough for reasoning distillation, mostly came from domain-specific settings and needs reevaluation for general reasoning.
    • Large language models often overthink and produce excessively long reasoning traces, which increases inference cost and reduces deployment efficiency in practice.
    • The authors built NaturalThoughts, a dataset that annotates reasoning traces generated by DeepSeek-R1, the teacher, for diverse NaturalReasoning questions by domain, meta-reasoning strategy, and verbosity.
    • They systematically explored data selection strategies based on diversity, such as question topic, semantic embedding, and reasoning strategy, as well as difficulty, such as answer length, verbosity, and model agreement.
    • They proposed and evaluated a mixed System 1/System 2 distillation approach, including random and difficulty-based mixtures, so student models can adjust reasoning depth at inference time.
    AbstractPDF
  25. 070Research Agents / SurveyarXiv:2506.18096

    Deep Research Agents: A Systematic Examination And Roadmap

    • Beyond existing LLMs and RAG, there is no clear definition or taxonomy for a new class of AI systems that integrate dynamic reasoning, adaptive planning, and iterative tool use.
    • There is a need to unify scattered research efforts into a single framework in order to understand common architectural patterns and foundational technologies.
    • Existing evaluation benchmarks do not adequately assess the full end-to-end capabilities of advanced AI research agents.
    • This paper formally defines deep research, or DR, agents and proposes a unified taxonomy that distinguishes static from dynamic workflows and single-agent from multi-agent systems.
    • It systematically analyzes foundational technologies, including information acquisition strategies such as API-based versus browser-based approaches, diverse tool-use capabilities such as code interpreters and multimodal processing, and memory mechanisms for optimizing long context.
    • It evaluates current tuning paradigms such as SFT, RL, and nonparametric continual learning, reviews key industry implementations, and critically assesses existing benchmarks for DR agents.
    AbstractPDF
  26. 069Security / AgentsarXiv:2506.23260

    From Prompt Injections to Protocol Exploits: Threats in LLM-Powered AI Agents Workflows

    • The rapid spread of LLM-based AI agents and their complex ecosystems lacks standardized, robust security practices, leading to widespread vulnerabilities.
    • Current integrations often rely on ad hoc authentication and weak verification mechanisms, making them highly vulnerable to many forms of exploitation.
    • Existing security research on LLM agents is fragmented and has not provided a unified end-to-end framework that covers threats across the full communication stack, from host-to-tool interactions to agent-to-agent interactions.
    • A unified end-to-end threat model was developed that systematically analyzes security risks across the entire LLM agent ecosystem, including host-to-tool and agent-to-agent communication.
    • More than 30 different attack techniques were classified into four major areas: input manipulation, model corruption, system and privacy attacks, and protocol vulnerabilities.
    • Rigorous mathematical formulations were provided for each threat category, explicitly defining attacker capabilities, goals, and affected layers to enable systematic analysis.
    AbstractPDF
  27. 068RAG / AgentsarXiv:2506.21931

    ARAG: Agentic Retrieval Augmented Generation for Personalized Recommendation

    • Existing RAG systems for personalized recommendation rely on simple retrieval mechanisms, such as cosine similarity, that fail to capture nuanced user preferences.
    • Existing RAG methods struggle to understand implicit preferences, process long-form user documents, and dynamically rank items based on multifaceted contextual factors.
    • Using LLMs directly for recommendation is computationally expensive, prone to hallucination, and constrained by fixed knowledge cutoffs, so robust grounding is needed.
    • ARAG proposes a multi-agent framework in which specialized LLM agents collaborate to refine context retrieval and item ranking for personalized recommendation.
    • It uses a User Understanding Agent (UUA), a Natural Language Inference (NLI) agent, a Context Summarization Agent (CSA), and an Item Ranking Agent (IRA) to decompose the complex recommendation task.
    • The agents operate in a blackboard-style collaboration, with UUA and NLI running in parallel, followed by CSA and then IRA, to synthesize user preferences and align them with candidate items.
    AbstractPDF
  28. 067Scientific AI / SurveyarXiv:2507.01903

    AI4Research: A Survey of Artificial Intelligence for Scientific Research

    • Despite rapid progress in large language models and their scientific innovation applications, there has been no comprehensive and systematic survey that specifically covers the full cycle of scientific research, AI4Research.
    • Existing surveys usually focus on narrower subareas such as AI for scientific discovery, which has fragmented understanding of AI's broader impact on academic workflows.
    • The lack of an integrated perspective hinders systematic understanding and slows further progress in using AI across all stages of scientific research.
    • The authors developed a taxonomy that categorizes AI applications into five major tasks across the research cycle: scientific understanding, academic surveying, scientific discovery, academic writing, and academic peer review.
    • They formally defined AI4Research and distinguished it from the narrower AI4Science, while providing a rigorous component-level framework for understanding AI's role at each stage.
    • For each component, they conducted an extensive literature review to synthesize current methods, pioneering work, and empirical summaries, and they also organized practical resources such as tools, datasets, and benchmarks.
    AbstractPDF
  29. 066Agents / SLMsarXiv:2506.02153

    Small Language Models are the Future of Agentic AI

    • The current dominant LLM-centric paradigm for AI agents carries high economic cost because reasoning demands enormous compute.
    • General-purpose LLMs are operationally inefficient and add latency when they perform the repetitive, specialized sub-tasks common in agentic workflows.
    • The large energy consumption associated with LLMs raises sustainability concerns, and they often lack precise specialization for narrow agentic tasks.
    • This paper proposes a paradigm shift to specialized small language models as the primary compute unit for most agentic calls.
    • It advocates a heterogeneous agent architecture that uses SLMs for routine and specialized tasks and reserves LLMs for complex general reasoning.
    • It provides a practical six-step algorithm for converting LLM-centric agent applications into SLM specialists, including data collection, task clustering, fine-tuning, and continual improvement.
    AbstractPDF
  30. 065Reasoning / Small ModelsarXiv:2504.15777

    Tina: Tiny Reasoning Models via LoRA

    • Achieving robust multi-step reasoning in large language models remains a major challenge.
    • Current LLM reasoning enhancement methods, such as supervised fine-tuning and reinforcement learning, are resource-intensive and often require substantial compute and large models.
    • The high cost and complexity of RL pipelines limit both accessibility and broad research on RL-based reasoning for language models.
    • It uses the small 1.5-billion-parameter base model DeepSeek-R1-Distill-Qwen-1.5B, which is known for its initial reasoning aptitude.
    • It applies Low-Rank Adaptation (LoRA) for parameter-efficient reinforcement learning with a GRPO-style algorithm, updating only a subset of the model parameters.
    • To ensure cost efficiency and direct comparability with existing models, it uses a minimalist training setup with two NVIDIA L40S GPUs and public reasoning datasets.
    AbstractPDF
  31. 064RL / InferencearXiv:2504.16084

    TTRL: Test-Time Reinforcement Learning

    • Applying reinforcement learning to reasoning tasks for large language models is bottlenecked by the high cost and scarcity of human-annotated ground-truth labels needed for reward estimation.
    • Existing RL methods for LLMs rely heavily on supervised reward signals, which makes it difficult to keep improving on newly encountered or unlabeled test data.
    • The lack of explicit reward signals at inference time limits an LLM's ability to adapt to new distributions and improve generalization without human intervention.
    • It introduces Test-Time Reinforcement Learning, or TTRL, a framework that lets an LLM update its parameters and improve generalization at test time using RL.
    • It generates reward signals without explicit ground-truth labels by repeatedly sampling model outputs and deriving a consensus output through majority vote.
    • It computes a rule-based binary reward from whether each sampled output matches the majority-vote pseudo-label, and uses it to optimize the model policy with an RL algorithm such as GRPO.
    AbstractPDF
  32. 063Vision-LanguagearXiv:2504.16072

    Describe Anything: Detailed Localized Image and Video Captioning

    • Existing vision-language models struggle to generate detailed and accurate captions for specific regions in images and videos.
    • High-quality datasets for training detailed local captioning models are limited.
    • Current evaluation metrics often rely on reference captions that lack comprehensive detail.
    • It developed the DAM architecture for multi-granularity region captioning, with focal prompts and a localized vision backbone.
    • It built the SSL-based data pipeline DLC-SDP using segmentation datasets and unlabeled web images.
    • It introduced DLC-Bench for evaluating detailed local captions without reference captions.
    AbstractPDF
  33. 062GUI AgentsarXiv:2501.12326

    UI-TARS: Pioneering Automated GUI Interaction with Native Agents

    • Existing GUI agents rely heavily on text-based representations and modular frameworks, which limit scalability.
    • There is a lack of large, high-quality data for training end-to-end GUI agents.
    • Complex GUI scenarios require advanced perception, reasoning, and coordination capabilities that current approaches do not have.
    • We developed UI-TARS, an end-to-end model that interacts with interfaces by processing screenshots directly.
    • We built large-scale datasets for perception, grounding, and action traces through automatic collection and curation.
    • We integrated System 2 reasoning through thought augmentation and reflection tuning.
    AbstractPDF
  34. 061LLMs / EfficiencyarXiv:2504.12285

    BitNet b1.58 2B4T Technical Report

    • Large language models are compute-intensive, requiring substantial memory and energy and causing high latency.
    • These resource demands constrain LLM deployment on edge devices and in real-time applications.
    • Previous 1-bit quantization methods either hurt performance, as in post-training quantization, or failed to scale to competitive model sizes.
    • It implements a modified Transformer architecture with custom BitLinear layers for 1.58-bit weights and 8-bit activations.
    • It uses a multi-stage training approach that begins with an unprecedented 4 trillion-token pretraining run and continues with supervised fine-tuning and Direct Preference Optimization.
    • It develops and open-sources a specialized inference library with custom kernels for efficient W1.58A8 operations on both GPUs and CPUs.
    AbstractPDF
  35. 060Reasoning / RLarXiv:2504.13837

    Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?

    • It is commonly believed that, like traditional RL discovering new strategies, RLVR gives LLMs new reasoning abilities.
    • No rigorous study has determined whether current RLVR truly expands the range of problems LLMs can solve or merely optimizes sampling of existing solutions.
    • Traditional evaluation metrics often fail to capture an LLM's full reasoning potential or its reasoning capability boundary.
    • Pass@k is introduced as a robust metric that defines the LLM's reasoning capability boundary by measuring the fraction of problems solvable within k attempts.
    • The study systematically evaluates reasoning ability across diverse LLM families, sizes, multiple RL algorithms such as PPO and GRPO, and task domains such as math, code, and visual reasoning.
    • Deep analysis methods, including perplexity analysis and solvable-problem coverage, are used to determine whether RLVR-generated reasoning paths are genuinely novel or already lie within the base model's distribution.
    AbstractPDF
  36. 059Architecture / TrainingarXiv:2503.24322

    NoProp: Training Neural Networks without Full Back-propagation or Full Forward-propagation

    • Traditional backpropagation is not biologically plausible because of the weight transport problem and the need to synchronize forward and backward passes.
    • Full backpropagation requires storing intermediate activations, which creates substantial memory overhead and limits true parallelization across network layers because of sequential dependencies.
    • The global error signal and sequential credit assignment in backpropagation can cause problems such as catastrophic forgetting and interference in lifelong learning settings.
    • It introduces NoProp, a block-wise neural network learning method inspired by denoising diffusion and flow matching models.
    • Each network block independently learns to denoise a noisy version of the target label using local backpropagation only within that block.
    • It broadcasts the input and target labels to all blocks, allowing each block to optimize its objective independently without global error-signal propagation.
    AbstractPDF
  37. 058Agents / Self-AwarenessarXiv:2504.03553

    Agentic Knowledgeable Self-awareness

    • Current LLM agents lack situational self-awareness, so they use cognitive resources such as reflection and external knowledge in a scattered and inefficient way.
    • Existing agent training methods are vulnerable to unexpected signals, prone to pattern collapse, and often incur high reasoning costs because they rely on a one-size-fits-all approach.
    • Agents also struggle to assess their own abilities dynamically in complex and changing environments, which leads to suboptimal decisions.
    • KnowSelf introduces a three-stage situational classification scheme, Fast, Slow, and Knowledgeable Thinking, to determine an agent's real-time cognitive resource needs.
    • It builds a lightweight offline knowledge system and generates self-awareness data by appending special tokens that indicate the type of thinking required for the trajectories the agent explored.
    • A two-stage fine-tuning process, SFT and RPO, teaches agents to connect situational cues with the right cognitive response, enabling them to activate reflection or external knowledge dynamically during inference.
    AbstractPDF
  38. 057Knowledge Graphs / LLMsarXiv:2504.03137

    LightPROF: A Lightweight Reasoning Framework for Large Language Model on Knowledge Graph

    • Existing KG-enhanced LLM approaches rely on large, computationally expensive models and complex KG traversal strategies.
    • Current methods do not exploit structural information in knowledge graphs effectively, which leads to redundant information and inaccurate reasoning.
    • It is a three-module framework that combines reasoning-graph retrieval, knowledge embedding through a small Transformer adapter, and mixed-prompt reasoning.
    • It is a parameter-efficient training method that freezes the LLM parameters and updates only the Knowledge Adapter.
    • It improves knowledge integration by converting both text content and graph structure into embeddings that are used as soft prompts.
    • It uses constrained breadth-first search with LLM-based scoring to retrieve relevant reasoning paths in the knowledge graph.
    AbstractPDF
  39. 056Pre-training / ReasoningarXiv:2504.04022

    Rethinking Reflection in Pre-Training

    • There is a common belief that complex cognitive abilities such as reflection in large language models emerge mainly during post-training stages like fine-tuning and reinforcement learning.
    • There is no systematic, scalable method for measuring reflection across different stages of pretraining.
    • Benchmarking reflection with standard reasoning datasets is difficult because reflective behavior is rare and error patterns are diverse.
    • To provide a finer-grained measurement framework, it defines multiple dimensions of reflection: situational, self, explicit, and implicit.
    • To systematically elicit and measure reflection by introducing human-like errors into reasoning chains, it develops a programmatic method for generating adversarial chain-of-thought datasets.
    • It uses a prompt-based LLM classifier to detect explicit reflection and measures four key metrics: accuracy, explicit reflection rate, explicit reflection accuracy, and implicit reflection accuracy.
    AbstractPDF
  40. 055Reasoning / EfficiencyarXiv:2504.05185

    Concise Reasoning via Reinforcement Learning

    • Large language models strengthened for reasoning tasks through reinforcement learning, or RL, tend to produce excessively verbose chain-of-thought outputs, which increases compute cost and inference latency.
    • The prior literature contains conflicting observations about whether response length is necessary for reasoning accuracy, with some studies finding that longer responses improve accuracy while others observe diminishing returns.
    • The fundamental mechanism by which RL training affects response length, and whether this verbosity is an essential part of reasoning or merely a byproduct, is not well understood.
    • The paper casts reasoning as a Markov decision process and performs a theoretical analysis of the loss dynamics of Proximal Policy Optimization, or PPO, and Generalized Reinforcement Learning with Policy Optimization, or GRPO, to explain how negative rewards push the policy toward verbosity.
    • It proposes a new two-stage RL training procedure that first strengthens reasoning on hard problems and then explicitly enforces conciseness on a small dataset of problems that are sometimes solvable.
    • It uses a built-in property of PPO, with generalized advantage estimation lambda below 1, where positive rewards encourage shorter responses and negative rewards encourage longer ones, to guide the model toward conciseness without sacrificing accuracy.
    AbstractPDF
  41. 054InterpretabilityarXiv:2504.07096

    OLMoTrace: Tracing Language Model Outputs Back to Trillions of Training Tokens

    • LLMs behave as black boxes, making it hard to understand why they produce specific outputs.
    • Existing interpretability methods such as influence functions are computationally intractable at the trillions-of-token scale of modern LLM training datasets.
    • The lack of tools to efficiently analyze memorization in LLMs, trace factual claims back to sources, or identify the origins of generated text raises concerns about transparency, copyright, and PII.
    • OLMoTrace developed a system that uses an expanded infini-gram index to efficiently find maximal exact-match spans within a trillion-token-scale training dataset.
    • The method has five stages: identify maximal matching spans, filter long and unique spans, retrieve containing documents, merge duplicates, and rerank documents by relevance using BM25.
    • The production system uses high-IOPS SSDs on Google Cloud to guarantee the disk I/O throughput needed for real-time performance on large-scale data.
    AbstractPDF
  42. 053Reasoning / SurveyarXiv:2503.24377

    Harnessing the Reasoning Economy: A Survey of Efficient Reasoning for Large Language Models

    • Current LLMs show inefficient reasoning behaviors such as excessive computation and unnecessary deliberation.
    • There is a lack of systematic understanding of how to optimize the tradeoff between reasoning ability and compute cost.
    • The survey develops a structured framework that classifies reasoning inefficiency and potential solutions.
    • It classifies optimization approaches into behavior regulation after training and usage improvement at test time.
    • Reasoning inefficiency arises from both model behavior, such as length bias and deceptive thinking, and model usage, such as poor algorithm choice and compute allocation.
    • The proposed solutions should address both post-training factors, such as data quality and structure, and test-time strategies such as budget allocation and adaptive decoding.
    AbstractPDF
  43. 052Reasoning / EfficiencyarXiv:2504.00810

    Z1: Efficient Test-time Scaling with Code

    • Large language models consume too many tokens during reasoning tasks, even on simple problems that do not need elaborate thinking steps.
    • Prior approaches lack an efficient way to scale test-time reasoning while controlling token usage.
    • The model is fine-tuned on a carefully selected code reasoning dataset of 107K examples with balanced short and long trajectories.
    • The method implements a Shifted Thinking Window that limits reasoning tokens and forces a direct answer once the threshold is exceeded.
    • It removes context separators so that reasoning depth can be adjusted more flexibly.
    • The results suggest that longer reasoning trajectories and larger dataset scale are key to eliciting effective reasoning.
    AbstractPDF
  44. 051Search / AgentsarXiv:2503.20201

    Open Deep Search: Democratizing Search with Open-source Reasoning Agents

    • Existing proprietary search AI solutions limit transparency, innovation, and community-driven development.
    • Current open-source search tools lack sophisticated query processing and contextual retrieval capabilities.
    • It presents Open Search Tool, which includes query reformulation, advanced retrieval, and context augmentation.
    • It presents two reasoning agent architectures, ReAct and CodeAct, that interpret queries and coordinate tool use.
    • It is a plug-and-play framework that can integrate with any underlying language model.
    • Adaptive web search based on result quality improves both efficiency and accuracy.
    AbstractPDF
  45. 050InterpretabilityarXiv:2504.02732

    Why do LLMs attend to the first token?

    • The persistent, unexplained phenomenon in which large language models disproportionately attend to the first token, called an attention sink, lacked a functional explanation.
    • Information degradation in deep Transformers, including rank collapse, representation collapse, and over-squashing, causes loss of individual token information, especially in long sequences.
    • Prior literature did not provide a fundamental understanding of why attention sinks occur or what functional purpose they serve.
    • This paper theoretically connects attention sinks to excessive mixing and representation collapse, and derives a new over-squashing bound that quantifies how sinks regulate information flow.
    • We performed extensive empirical validation on state-of-the-art LLMs Gemma 7B and the LLaMa 3.1 family through perturbation analysis, close inspection of attention patterns, and targeted ablation studies.
    • Controlled pretraining experiments demonstrated a causal relationship between context length, model scale, and the formation and strength of attention sinks.
    AbstractPDF
  46. 049Medical AI / RAGarXiv:2503.23513

    RARE: Retrieval-Augmented Reasoning Modeling

    • LLMs exhibit knowledge hallucination and insufficient reasoning ability in specialized domains.
    • Existing approaches such as RAG mainly supplement knowledge at inference time and do not systematically train domain-specific reasoning during learning.
    • Conventional fine-tuning or pretraining for domain knowledge is costly, causes knowledge entanglement, and remains vulnerable to hallucination because of parameter budget limits.
    • It externalizes domain knowledge into searchable sources and internalizes domain-specific reasoning patterns inside the LLM.
    • It distills high-quality step-by-step reasoning responses that include retrieved knowledge using a teacher model.
    • It trains a lightweight student model with a masked-loss objective that injects retrieved knowledge into the prompt, forcing the model to focus on integrating the provided information and reasoning from it.
    AbstractPDF
  47. 048Research AgentsarXiv:2503.22708

    CodeScientist: End-to-End Semi-Automated Scientific Discovery with Code-based Experimentation

    • Existing autonomous scientific discovery, or ASD, systems often focus on only part of the research pipeline or rely on domain-specific languages, which limits the range of possible discoveries.
    • There is a need for an end-to-end semi-automated system that uses large language models for scientific discovery while emphasizing code-based experimentation.
    • Evaluating the reliability and reproducibility of discoveries made by LLM-based ASD systems is difficult because of the inherent variability of language model outputs and the potential unreliability of generated code.
    • CODESCIENTIST implements a semi-automated end-to-end scientific discovery workflow consisting of ideation, planning, experiment design and execution, reporting, and meta-analysis.
    • It uses a mutator-style LLM genetic search approach for ideation, combining fragments of research papers and code blocks to generate diverse research ideas.
    • The system generates and runs code in an instrumented sandbox, uses iterative generate run reflect debug loops, and performs multiple independent executions for each experiment to ensure reproducibility.
    AbstractPDF
  48. 047LLMs / EnterprisearXiv:2504.00698

    Command A: An Enterprise-Ready Large Language Model

    • General-purpose LLMs often lack optimization for enterprise workflows such as retrieval-augmented generation, complex agentic tasks, and robust multilingual support.
    • Deploying state-of-the-art LLMs in enterprise settings is often compute-intensive and expensive, which hinders on-premise or privacy-preserving deployment.
    • Integrating diverse capabilities into a single LLM without catastrophic forgetting or performance tradeoffs remains a major challenge in large-scale model training.
    • Command A balances efficiency and performance with a decoder-only Transformer that uses a new hybrid attention mechanism and Grouped-Query Attention.
    • Its distributed post-training approach trains six specialized expert models, such as Code, Safety, and RAG, on domain-specific data and then merges their parameters into a unified soup model with integrated capabilities.
    • The refinement stage integrates advanced supervised fine-tuning and reinforcement-learning techniques, including Self-improving Robust Preference Optimization and online RLHF for human alignment and specific abilities.
    AbstractPDF
  49. 046Benchmarks / Research AgentsarXiv:2504.01848

    PaperBench: Evaluating AI's Ability to Replicate AI Research

    • Existing AI agent benchmarks are often too narrow and too simple to evaluate performance on long-horizon, open-ended machine learning R&D tasks.
    • In particular, safety and governance frameworks need rigorous, objective, and scalable measures of an AI model's autonomy and ability to conduct scientific research.
    • Human reproduction of research papers is time-consuming and subjective, which makes it hard to scale for consistent evaluation of AI agents.
    • OpenAI researchers introduced PaperBench, a benchmark of 20 recent ICML papers that requires AI agents to reproduce empirical results from scratch without access to the original codebase.
    • For each paper, a detailed hierarchical rubric developed with the original authors provides binary pass or fail criteria across 8,316 specific contribution items.
    • SimpleJudge, an LLM-based automatic judge validated at an F1 score of 0.83 against human grading benchmark JudgeEval, provides scalable and consistent evaluation of agent submissions.
    AbstractPDF
  50. 045Image GenerationarXiv:2502.17437

    Fractal Generative Models

    • Traditional generative models struggle with the computational burden and artificial sequentialization required by high-dimensional, non-sequential data such as raw images.
    • Achieving high-fidelity pixel-level image generation at high resolution is computationally infeasible and difficult because of the enormous dimensionality.
    • Modularity in existing deep learning is often limited to layers or simple blocks, leaving it without the higher-level abstractions needed to compose full generative models.
    • It proposes a new generative modeling paradigm, 'fractal generative models,' that recursively calls existing generative models as atomic generation modules to form self-similar structures.
    • The framework is instantiated as autoregressive (AR) models for pixel-level image generation, FractalAR and FractalMAR, using a divide-and-conquer strategy over image patches.
    • At each hierarchical level, it uses multi-scale patching and local attention mechanisms to greatly reduce the quadratic computational cost of high-resolution image generation.
    AbstractPDF