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按编号从大到小列出全部收录论文及摘要。

收录论文 1,430资料更新日期 Research Monitoring基准测试
  1. 882RoboticsarXiv:2608.11204

    Surgical WAM: A World-Action Model for Data-Efficient Surgical Robot Learning

    • The authors propose a two-stage learning method that first learns visual dynamics in surgical scenes from unlabeled endoscopic video and then fine-tunes on a small amount of labeled action data.
    • Video pretraining raises the average success rate on four SurRoL benchmark tasks from 63.5 percent to 77.8 percent.
    • On PegTransfer, it improves by 20 percentage points, from 66 percent to 86 percent, and the effect is strongest on tasks that involve many contacts and two-handed cooperation.
    • The video-pretrained model reaches higher performance with only half the fine-tuning budget, which greatly improves training efficiency.
    • The same trend appears on real surgical robot data from JIGSAW, which confirms that the effect is real and not just a simulator artifact.
    原文摘要PDF
  2. 881RoboticsarXiv:2608.10860

    Flex-$π$: A Multi-Stream World-Action Model with Compute Flexibility

    • It discovers a free lunch in which a video-generation model's VAE can encode not only RGB but also 3D point maps with almost no loss.
    • It builds a multi-stream world-action model that predicts RGB, 3D depth, and DINO object features together in a shared latent space.
    • During training it randomly drops modalities, or stream dropout, so that one checkpoint works with any combination at inference time.
    • On robot tasks in RoboTwin, with only 50 demos, it is 1.9x better than existing WAMs, and on a real dual-arm robot it achieves 2x to 7x higher success.
    • In action-only mode it is faster than π0.5 and still achieves higher success, at about 60 ms.
    原文摘要PDF
  3. 880AI / GeneralarXiv:2608.11095

    Why Does CLAUDE.md Keep Growing? Catastrophic Remembering in Agentic Coding

    • It tracked 247,694 instructions across 1,867 GitHub repositories and showed that agent prompts grow by 226% over their lifetime while the deletion probability only keeps falling.
    • The cause is incomplete recall: once the reason for adding an instruction disappears, it cannot be safely deleted, so additions continue indefinitely, with O(2^|D|) complexity.
    • 76.8% of instruction deletions happen in a single commit that rewrites the entire file, so individual deletions are rare.
    • If you annotate the prompt with why each instruction is needed, you can remove 99.3% of unnecessary instructions, reducing growth from +211.3% to +1.4%.
    • On the real WildIFEval benchmark, prompt annotations improved instruction-following accuracy by up to 23.1%.
    原文摘要PDF
  4. 879AI / GeneralarXiv:2608.10915

    ComBodied Agents: a New Paradigm of Human-Centric Agentic AI

    • It proposes the Combodied Agents paradigm, which goes beyond the digital-agent and robot-agent split by treating the human state trajectory itself as the primary modeling target.
    • It designs an integrated closed loop of multimodal perception, long-term memory, personal world models, adaptive intervention policies, and feedback.
    • It clearly distinguishes this from human digital twins: it uses uncertainty-aware representations tailored to the task and does not require full replication.
    • It organizes the full design space, including edge-native personal models, scenario-centered evaluation, autonomy-preserving metrics, and governance directions.
    • It argues that success should be defined not by replacement of human work, but by improvement of human capability and judgment.
    原文摘要PDF
  5. 878AI / GeneralarXiv:2608.08888

    Full-bandwidth transformer

    • We introduce latent feedback decoding, which feeds the full top hidden state from the previous token directly into the next input.
    • It adds less than 1% extra inference cost while preserving the standard Transformer structure and KV cache.
    • At 1B parameters and 400B training tokens, it consistently improves validation loss, math and code generation, and instruction following.
    • It saves about 1.5x training tokens for the same performance and in some tasks approaches models trained with 5x more tokens.
    • When latent feedback is enabled, the reasoning trace becomes shorter while accuracy is maintained or improved.
    原文摘要PDF
  6. 877Computer VisionarXiv:2608.10744

    Beyond Pixels: From Video Priors to 4D Worlds

    • It proposes Latent-to-4D, a framework that connects the VAE latent space of video generation models directly to a 4D decoder.
    • It skips the step of generating RGB images, which avoids information loss and error accumulation.
    • It is trained on about 1,000 existing reconstruction clips and can be reused across multiple video generators with a single checkpoint.
    • On the Text4D-200 benchmark, it improves DINO-F1 by 2.88 to 3.45 points, and on I4D-200 it improves by 5.81 points.
    • Human evaluation shows that it outperforms prior methods in geometric validity, completeness, and temporal stability.
    原文摘要PDF
  7. 876Neural NetworksarXiv:2608.09888

    BDH-CQ: In-Context Learning with Recurrent Latent Reasoning

    • It proposes a 'latent reasoning' approach that performs recurrent reasoning in hidden state space without outputting tokens.
    • With a very small 150M-parameter model, it reaches 29.5% accuracy on ARC-AGI-1 at a cost of $0.0007 per task.
    • It sets a new cost-accuracy standard while cutting cost to about one-thousandth of the previous best system.
    • It systematically analyzes the strengths and limits of in-context learning from demonstrations alone, using experiments with color mapping, boundary propagation, rotation, and other compositions.
    • It designed an architecture that can scale naturally to 1 trillion parameters, and early experiments confirm Transformer-like scaling laws.
    原文摘要PDF
  8. 875LLMs / NLParXiv:2608.09096

    Evo-Bench: Can Language Models Improve Agent Harness?

    • It creates the first benchmark for evaluating whether an LLM can autonomously improve the agent harness, meaning the execution code.
    • It introduces a strict design that avoids overfitting through harness-sensitivity-based task selection and separate validation and evaluation splits.
    • Evaluation on nine frontier models shows gains of up to 16.6 points and comes close to the human-designed baseline.
    • In the general domain, autonomous evolution beats the human design, but it shows limits in office workflows.
    • It confirms that the evolved harness acts as a transferable reasoning structure for other policy models.
    原文摘要PDF
  9. 874AI / GeneralarXiv:2608.09703

    Matryoshka Language Model Suites

    • We propose the Matryoshka framework, which trains submodels of different sizes end to end in a single nested structure.
    • It reduces total training compute by 36% while matching the performance and validation and out-of-domain perplexity of independently trained models.
    • Free distillation from the largest model to the smaller ones happens automatically at every step.
    • The nested draft models improve speculative decoding throughput by 14% to 26%.
    • Extensive ablations validate the importance of width and depth settings, the junction mechanism, and the distillation loss.
    原文摘要PDF
  10. 873Computer VisionarXiv:2608.09873

    Sci-VBench: Evaluating Knowledge- and Reasoning-Intensive Video Generation in Science Domains

    • We built 1,253 expert-annotated video-generation tasks across 60 courses in four fields: natural sciences, medicine, humanities and social sciences, and engineering.
    • We provide an expert-written evaluation rubric and step-by-step scoring criteria from 1 to 5 for every example.
    • Non-expert raters also achieve high agreement with experts when given the rubric, with Cohen's kappa = 0.842.
    • Evaluation of 16 frontier models reveals similar visual quality but large gaps in scientific accuracy.
    • Prompt improvements help somewhat, but the limits in spatial and temporal consistency come from the generator itself.
    原文摘要PDF
  11. 872AI / GeneralarXiv:2608.09629

    Rethinking Self-Evolving Agents: Do We Still Need Prescribed Optimization Pipelines?

    • It is the first systematic attempt to question whether framework-defined improvement procedures, or optimization meta-policies, are still necessary.
    • Using GPT-5.5, OEO beats the SKILLOPT and GEPA pipelines in 12 of 14 comparisons, with 1 tie and 1 loss.
    • OEO uses only 34.3 percent of the median tokens used by SKILLOPT, which demonstrates its efficiency.
    • For mid-sized models, prescribed pipelines work better, which reveals a model-capacity boundary for adaptive division of labor.
    • The prescribed procedures make optimization paths more consistent, but the path differences are larger than the final action differences.
    原文摘要PDF
  12. 871Computer VisionarXiv:2608.09438

    Unveiling the Secret of AdaLN-Zero in Diffusion Transformer

    • It breaks down the performance gains of adaLN-Zero into three factors: SE structure, zero initialization, and progressive updates.
    • Zero initialization is the most important factor, and the paper finds that the conditional modulation weights follow a Gaussian distribution after training.
    • It proposes adaLN-Gaussian, which initializes with a Gaussian distribution, to validate the analysis and obtain practical gains.
    • It proposes SE-adaLN-Zero, inspired by the SE structure, and achieves additional performance gains.
    • It confirms consistent improvements and generalization on four datasets, including ImageNet1K.
    原文摘要PDF
  13. 870Machine LearningarXiv:2608.09228

    Privileged Solutions or Context-Induced Teacher Behavior? Dissecting On-Policy Self-Distillation

    • It designs a controlled experiment, OP2SD, to distinguish whether OPSD's gains come from answer transfer or context-induced behavior.
    • Even when the teacher is given solutions to other math problems instead of the correct answer, the improvement is similar to OPSD.
    • When the context comes from physics rather than math, the effect weakens, showing that domain alignment in the context is necessary.
    • It confirms the same pattern across three Qwen3 models, 1.7B, 4B, and 8B, and on the AIME and HMMT benchmarks.
    • It shows that OPSD should not be interpreted only as privileged-information transfer, because context-induced behavior is also an important factor.
    原文摘要PDF
  14. 869Computer VisionarXiv:2608.09931

    Perception Before Supervision: Self-Contained Visual Distillation from Counterfactual Blind Spots

    • It proposes a three-stage verification criterion that finds visual blind spots using only the model's own counterfactual responses, without external models, annotations, or tools.
    • It performs contrastive distillation with an expanded view of the blind spot as the positive teacher and a removed view as the negative teacher.
    • On Qwen3-VL-8B, it improves all 12 benchmarks with no regression.
    • It records gains of +3.60 on OCRBench, +3.38 on fine-grained perception in MMStar, and +3.08 on logical reasoning.
    • It outperforms all six self-evolution baselines that depend on external GPT-4o supervision.
    原文摘要PDF
  15. 868Computer VisionarXiv:2608.09730

    World Tokens: Enhancing Embodied Policies with Training-Time World Modeling

    • It proposes the World Tokens architecture, which uses a video world model only during training and removes it at deployment.
    • The World Adapter converts VLM features into 256 tokens and conditions both video prediction and action generation on them.
    • The action expert cannot see the full VLM sequence directly, which prevents it from bypassing the world-model supervision.
    • With a 2B model, it reaches 98.2 percent on LIBERO, 71.5 percent on SIMPLER-WidowX, and 82.1 percent on SIMPLER-GoogleRobot.
    • On the real R1 Pro robot, it improves success from 59.4 percent to 76.0 percent compared with the matching baseline.
    原文摘要PDF
  16. 867AI / GeneralarXiv:2608.09836

    Mismatch Matters: On-Policy Distillation Beyond Token Agreement

    • We discovered a degenerate agreement phenomenon in which the student cheats by repeating tokens to achieve high token-level agreement with the teacher.
    • We distinguish between surplus tokens, which the teacher scores near zero, and missing tokens, which the teacher wants but the student does not produce.
    • We propose TIDE, which corrects surplus tokens with Hellinger-distance-based bounded suppression and missing tokens with analytical top-K injection.
    • It raises Avg@8 from 6.9% to 20.3% under strong teacher-student disagreement.
    • It also cuts average response length by 3.6x while greatly reducing format failures.
    原文摘要PDF
  17. 866RoboticsarXiv:2608.09892

    XPolicyLab: A Unified Standard and Open Ecosystem for Robot Policy Evaluation and Deployment

    • It proposes a unified standard that reduces the cost of connecting robot policies to environments from O(NM) to O(N+M).
    • It integrates 42 robot policies, including VLA, WAM, and imitation learning, behind one interface.
    • It creates a structure that isolates policy servers and environment clients according to software dependencies.
    • It cuts adaptation time for a new policy from more than 5 hours to 30 minutes when packaged skills are used.
    • It supports RoboTwin and RoboDojo simulation, as well as real robot evaluation, through one adapter.
    原文摘要PDF
  18. 865RoboticsarXiv:2608.09853

    RynnValue: Scaling Robotic Value Foundation Models with Temporal Distance

    • It changes the supervision target for reward models from progress to temporal distance, meaning cost-to-go to the goal, so scaling no longer requires preference labels.
    • It trains on heterogeneous robot data covering 7,000 hours and about 3 million clips.
    • Time-order shuffling and value-isolation attention keep the model from relying only on the learned values.
    • It surpasses the previous best preference-labeled model, 0.655, by reaching 0.675.
    • In real robot RL, it improves success rates from 52.5 percent to 72.5 percent online and from 63.8 percent to 82.5 percent offline.
    原文摘要PDF
  19. 864Computer VisionarXiv:2608.09926

    Learning How the World Evolves: Extrapolative Video World Models via Latent Dynamics Reasoning

    • We propose LDR, the first video world model to explicitly infer the laws of motion in latent space.
    • The architecture enforces learning of physical laws by numerically integrating the lower-order dynamics and having the model regress only the third- and higher-order residual that drives the rollout.
    • It operates with 26x fewer parameters and 143x faster speed than video diffusion models.
    • Its out-of-distribution error gap is more than 20x smaller, showing strong extrapolation ability.
    • It accurately generalizes learned dynamics even when color, shape, and orientation change.
    原文摘要PDF
  20. 863Computer VisionarXiv:2608.09449

    Sekai2: From World Exploration to Interactive World Modeling

    • It provides 2,826 hours of real-world video, 128,892 clips, and camera trajectories with hierarchical annotations across 113 countries.
    • It creates 649,597 time-level segments that separate subject motion, environmental change, static scenes, and camera behavior.
    • It offers 982 loop videos revisiting 360-degree panoramas, which provide supervision for long-range spatial consistency.
    • It cross-validates that the captions generated independently of camera annotations are aligned with actual camera motion.
    • It provides a scalable data foundation for video generation, camera control, and pretraining interactive world models.
    原文摘要PDF
  21. 862AI / GeneralarXiv:2608.09119

    Motif 3: Technical Report

    • It uses an ultra-fine MoE architecture that selects only 8 experts per token out of 384, increasing capacity while reducing compute.
    • Grouped Differential Latent Attention, or GDLA, achieves both noise suppression and KV cache compression.
    • It is pretrained on 12.5 trillion tokens and then uses multi-teacher on-policy distillation, or MOPD, to unify diverse capabilities into one model.
    • It supports a 256K context length and stabilizes large-scale training with MXFP8 precision.
    • Among open-weight models, it shows competitive performance on agent tasks, math reasoning, and scientific knowledge.
    原文摘要PDF
  22. 861Machine LearningarXiv:2608.09819

    Macaron-V1: Towards Open Continual Learning with Self-Improvement and Mixture-of-LoRA

    • The system uses a Mixture-of-LoRA structure that keeps the base model frozen and selects expert adapters for chat, agent, coding, or GenUI on each turn.
    • It proposes a framework for continual learning after deployment through joint model and harness design plus a recursive self-improvement loop.
    • It releases Venti, a 744B model based on GLM-5.2, and Tall, a 50B model based on Qwen3.6.
    • It also develops UI4A, a component-native GenUI harness, and MindForge, an agent RL framework.
    • It shows competitive performance with frontier models on Personal Intelligence, GenUI, and general capability benchmarks.
    原文摘要PDF
  23. 860RoboticsarXiv:2608.09381

    JEPA-WAM: Learning Vision-Language-Action Policies with Joint-Embedding World Modeling

    • It proposed a latent world-action model that predicts relationships between current and future spatial structure in a pretrained V-JEPA representation space, without generating video directly.
    • It combined transition prediction and continuous action generation in a single predictor so that future supervision directly refines the core structure that forms action representations.
    • On LIBERO-Plus, the setting without prior robot-policy training achieved 79.2%, and the setting combined with π0.5 achieved 86.3%, each yielding the highest result.
    • It also generalized to changes in visual and spatial conditions on RoboTwin 2.0 and a real dual-arm robot, showing potential for robot control without the cost of video generation.
    • Because the transition objective learns shared visual structure independent of language, it may lack expressive power for tasks where different instructions produce substantially different futures from the same scene.
    原文摘要PDF
  24. 859LLMs / NLParXiv:2608.09802

    SWE-Bench ProMax: Benchmarking Agents on Large-Scale Multilingual Code Refactoring

    • It quantifies the problem that 60% of existing benchmarks contain bad tests that are either too narrow or too broad.
    • It builds a multilingual refactoring benchmark across seven programming languages: Python, Java, TypeScript, Go, C, C++, and Rust.
    • The tasks are large, with an average of 11.4 files and 261.6 lines of code to change, which makes them more than three times more complex than prior benchmarks.
    • GLM-5 shows strong value for money by matching Claude Sonnet 4.6 at about one twentieth of the cost.
    • Failure analysis shows that the main problem is not modifying enough of the files that actually need to change.
    原文摘要PDF
  25. 858AI / GeneralarXiv:2608.09867

    Stealing Reasoning Traces from Proprietary LLM APIs

    • It discovers a fundamental design flaw in encrypted reasoning blocks that makes them interchangeable across sessions, users, and models.
    • It demonstrates a cross-model attack that turns the reasoning of a strong model into something that can be decoded by a weaker model against OpenAI, Anthropic, and Google.
    • It recovers real sensitive information from public developer logs, including 62 API keys, 33 passwords, and 30 emails.
    • It also confirms an invisible prompt-injection attack vector that passes through encrypted blocks.
    • It highlights the fundamental limits of encryption-based approaches and proposes concrete mitigation measures for both vendors and users.
    原文摘要PDF
  26. 857LLMs / NLParXiv:2608.06329

    Benchmarking the Benchmarks: Evaluating Benchmarks for Conversational Agents

    • It proposes an LLM-judge-based framework for evaluating the consistency, complexity, and policy coverage of conversational-agent benchmarks without reference labels.
    • It validates the framework with benchmarks generated by LLMs of different strengths and consistently distinguishes quality differences across benchmarks made by models with different capabilities.
    • The metrics also detect the expected drop in quality on deliberately degraded benchmarks.
    • Correlation tests against human annotation show a medium-to-strong statistically significant correlation.
    • It also applies the framework to manually curated benchmarks, such as tau3-bench, to demonstrate practical usefulness.
    原文摘要PDF
  27. 856Computer VisionarXiv:2608.07409

    UniJEPA: A Unified Joint-Embedding Predictive Architecture for Task-Agnostic Visual World Modeling

    • It proposes a single objective that jointly learns image-level transformation prediction and video-level temporal prediction in one latent space.
    • End-to-end training is possible without EMA, stop-gradient, or a pretrained encoder, and Gaussian regularization prevents representation collapse in a mathematically grounded way.
    • Photometric prediction learns invariant structure, temporal prediction learns equivariant dynamics, and the balance between them controls the level of abstraction.
    • It achieves competitive or better performance than individual JEPA models on ImageNet, Something-Something-v2, Epic-Kitchens, and control benchmarks.
    • After action-conditioned post-training, it supports zero-shot planning and plans up to tens of times faster than generative world models.
    原文摘要PDF
  28. 855Software EngineeringarXiv:2608.03392

    Self-Evolving Coding Agents

    • It clearly defines self-evolving coding agents as distinct from general coding agents and general self-evolving agents.
    • It classifies six evolution targets: framework, memory, skills and tools, model, workflow, and topology, which it calls an object-centered taxonomy.
    • It adds two auxiliary axes: when evolution happens, such as during a task, after a task, or step by step, and software-specific evidence, such as execution feedback, repository context, and coding traces.
    • It argues that executable feedback, repository-level context, and coding traces make software engineering a natural domain for agent self-evolution.
    • The paper's challenges are feedback reliability, benchmark overfitting, safety, maintainability, cost, and generalization, and the paper list is publicly available on GitHub.
    原文摘要PDF
  29. 854Machine LearningarXiv:2608.07110

    Modular TTT: Rethinking Test-Time Training as Composable Modules

    • It expresses the internal learner of TTT as a DAG and builds a modular framework where loss functions, learning rates, weight decay, and normalization can be adjusted independently.
    • Small learning-rate initialization, weight decay, and single-layer nonlinearity, specifically SiLU, consistently improve performance.
    • In contrast, deep fast-weight networks and normalization hurt performance because activation values become too large. Residual connections and gating have little effect.
    • The best variant trains 410M and 1.45B models on 100B tokens and reaches performance on par with Gated DeltaNet.
    • Developed at ByteDance, it releases an optimized implementation that improves training throughput several times over prior TTT systems.
    原文摘要PDF
  30. 853Computer VisionarXiv:2608.07468

    SimWAM: A Simple World Action Model for End-to-End Autonomous Driving

    • We propose SimWAM, a structure that uses video generation only as a training signal and removes it at inference time, so real-time video generation is unnecessary and inference is faster.
    • Because the video expert and action expert do not share parameters, it is easy to replace the video model with a better one or scale the action model.
    • On NAVSIM, it achieves 91.5 PDMS. It performs better than prior world-action-model-based planners while keeping inference latency much lower.
    • We additionally apply reinforcement learning (GRPO) to optimize complex driving rewards beyond trajectory imitation.
    • Zero-shot transfer to the nuScenes dataset also confirms generality.
    原文摘要PDF
  31. 852AI / GeneralarXiv:2608.07449

    SkillProx: Self-Evolving Agent Skills via Proximal Textual Gradient Descent

    • It formulates skill modification as a forward-backward optimization problem. The forward pass verifies diagnosis, revision, and execution, while the backward pass estimates the usefulness of each knowledge unit for integration or removal.
    • It introduces a result-based verification loop that reruns the same task after revision and rolls back if performance drops.
    • In the backward pass, it measures the contribution of each knowledge unit with a leave-one-out scheme, and removing bad knowledge improves accuracy from 46 percent to 54 percent.
    • Across three backbone LLMs, it achieves an average accuracy gain of 3.0 percentage points over the previous best method.
    • Unlike prior methods that only keep adding skills, its key distinction is that it explicitly asks what should be removed.
    原文摘要PDF
  32. 851Computer VisionarXiv:2608.07408

    Addressable Memory for Video World Models

    • We precisely diagnose the cause of long-term memory failure in video world models as distribution shift in positional encoding (RoPE).
    • WorldTrace assigns each memory summary a virtual position within the training range, restoring memory accessibility without extra training.
    • WorldTrace-Field improves temporal consistency by 15.5%, and WorldTrace-Landmark improves recall of previous scenes by 19.5%.
    • We propose LoopBench, a new benchmark that measures whether a model can recover after a long detour back to the original scene.
    • It was developed at NVIDIA and can be applied directly to existing models without retraining.
    原文摘要PDF
  33. 850LLMs / NLParXiv:2608.06867

    LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers

    • It proposes a unified framework that decomposes routers into five components, allowing more than 16 heterogeneous routers to be compared in the same language.
    • It automatically runs 18 models across a range of benchmark tasks to build routing evaluation data, xRouteBench, covering general NLP, vision, time series, and personalization.
    • The learned router improves by 14.6% relative to fixed model selection. However, when the cost constraint gets tight, the lighter router wins.
    • User-specific routing consistently produces personalization gains, but only when the user context is modeled well.
    • It releases the open-source LLMRouter library so that new routers can be added in a few lines.
    原文摘要PDF
  34. 849LLMs / NLParXiv:2608.06370

    The Bitter Lesson of Tool Calling

    • On 11 of 14 models, the Python code method matches or beats the JSON method in accuracy, and the GPT-5.6 family improves by up to 10.6%.
    • The code method is overwhelmingly better for chained tool calls, reaching 18.8% higher accuracy than JSON when the chain length is at least 12.
    • JSON misses calls when more than 70 tools are invoked at once, while the code method keeps 100% accuracy up to 100 tools.
    • Under context rot, the code method remains stable, while JSON drops by 2.3% on average and the filesystem method drops by 32%.
    • PTC performance is determined more by model generation time than by model family.
    原文摘要PDF
  35. 848ResearcharXiv:2608.05459

    Moduli of the Sourceless Framed Beltrami-Vekua Normal Form

    • It reduced the sourceless Beltrami-Vekua equation on a simply connected domain with boundary to a minimal form on the unit disk and precisely characterized the remaining symmetries.
    • When the coefficient field has no zeros, the pair ϑ and K completely classifies the equation, and it also determined the range of possible invariants.
    • In phase-integrable regions containing zeros, it expanded the classification data to the three quantities ϑ, dη, and d⋆η, separating vortex charge from the remaining phase curvature.
    • It explicitly constructed inequivalent equations with the same mass and charge, showing that those two numbers cannot capture the infinite-dimensional classification space.
    • Multiply connected domains require additional period data, and complete classification remains open for rough measurable coefficients or zeros that are not phase-integrable.
    原文摘要PDF
  36. 847Machine LearningarXiv:2608.06216

    Continual Learning in Transition

    • It argues that continual learning used to mean only parameter updates, but now it should also include elements outside the model, such as memory, skills, and protocols.
    • It proposes a three-axis framework, When, Where, and How, so that dozens of existing methods can be compared on a single map.
    • It summarizes that on-policy learning, where the model learns from its own experience, is better than off-policy methods for reducing forgetting.
    • It analyzes verifiable reward as the key bridge that lets agents improve themselves without human help.
    • It separates areas where research is dense from areas that are nearly empty, pointing out that system-level integrated learning is the main challenge ahead.
    原文摘要PDF
  37. 846Computer VisionarXiv:2608.05903

    Robust-WAM: Bridging Generative Pretraining and Semantic Foresight in World-Action Models

    • It proposed a post-training method that adds semantic information robust to appearance changes while preserving large-scale video pretraining in VAE-based world-action models.
    • It inserted training query tokens with temporal positions into the action stream and aligned them with DINOv3 semantic representations of future scenes, keeping additional inference cost low.
    • On LIBERO-Plus, it raised out-of-distribution success rates for FastWAM and GE-Act by 9.2 and 8.6 percentage points, respectively, while preserving performance under normal conditions.
    • With lighting-color changes on a real Franka robot, it raised GE-Act success from 57.3% to 80.0%, and the same method could be added to multiple world-action architectures.
    • The tested changes centered on lighting and color across two simulation benchmarks and one real robot, so robustness to more complex changes in objects, backgrounds, and actions needs further verification.
    原文摘要PDF
  38. 845Computer VisionarXiv:2608.06257

    MASS: Multiplayer World Models with Authoritative Shared State

    • It explicitly represents the world shared by multiple users as structured entity records, including coordinates, orientation, and whether an entity is alive, maintaining a single authoritative state.
    • It separates the Logic Engine, which updates state, from the Rendering Engine, which produces camera images, decoupling world-transition cost from the number of requested views.
    • In a matched Snake evaluation, it achieved state-reconstruction accuracy of 0.764, above the video-based baseline's 0.128, and progressed 1024 players for up to 10,000 steps while preserving structural validity.
    • It applied the same structure to multiple games through declarative schemas and also demonstrated clients predicting the shared state during network delays.
    • Current evidence is primarily limited to simple two-dimensional games and restricted entity interactions, and the authors left three-dimensional environments and more complex interactions as future work.
    原文摘要PDF
  39. 844Computer VisionarXiv:2608.05811

    Energy-Guided Flow Matching

    • It replaced flow matching's fixed clean-image endpoint with an endpoint that moves from a low-frequency image to the original, making generation proceed from coarse structure to detail.
    • It calculated frequency energy for each image and automatically adjusted heat-diffusion time so that the missing energy is released at the same rate.
    • On ImageNet 256×256, it achieved FID 1.55 after 200 training rounds and 1.45 after 600, improving both quality and training efficiency.
    • It achieved FID 1.58 at 512×512, and GenEval 0.85 and DPG-Bench 83.9 for text-to-image generation, without architectural changes or additional losses.
    • It has not yet been evaluated on video and time-varying signals, joint text-image models, embodied decision making, or the largest recent foundation models.
    原文摘要PDF
  40. 843Computer VisionarXiv:2608.05798

    KVAE: Family of Tokenizers for Multimodal Generative Models

    • It designed the KVAE tokenizer family with two compression ratios for video, eightfold compression for images, and 48kHz audio with a 50Hz latent representation.
    • In video and image reconstruction and generation evaluations, it reported results matching or exceeding public tokenizers such as Wan-2.2, HunyuanVideo-1.5, and FLUX.2.
    • The audio model has 166.9 million parameters, making it smaller than the larger comparison models, while showing preference advantages in multiple domains in human side-by-side listening evaluations.
    • It treated diffusability, whether the latent space is easy for a generative model to learn, as a design criterion alongside reconstruction quality, and released the code and models under the MIT License.
    • The audio FAD evaluator internally handles only 16–32kHz and therefore cannot measure full-band quality above 16kHz, while model-based aesthetic scores are also subject to evaluator bias.
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  41. 842Machine LearningarXiv:2608.06296

    On-Policy Self-Distillation without Any Supervision

    • It achieves self-distillation using only the model's own generations, without external answers, environment feedback, or a larger model.
    • It forms a pseudo-answer by majority vote and uses the shortest agreeing rollout as the teacher.
    • It applies distillation to the prefix of incorrect rollouts so that the model corrects the wrong part precisely.
    • On math benchmarks, it improves by 8.5 to 10.7 points over the base model without reasoning mode and by 3.2 to 2.3 points over OPSD.
    • Forward KL works best, reverse KL diverges during training, and JSD is ineffective.
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  42. 841Computer VisionarXiv:2608.06009

    Wan-Animate-2: Pushing the Application Boundaries of Character Animation

    • It removes the intermediate motion extractor, such as skeleton or SMPL, and feeds the driving video directly into the DiT.
    • A dual-branch design, temporal-alignment RoPE, and sparse reference attention balance fidelity and efficiency.
    • It introduces Viewpoint LoRA, which controls the output camera viewpoint from a text prompt.
    • Wan-Animate-2-Lite reaches 24 fps in real time through a three-stage training process of teacher forcing, error buffer, and self-forcing.
    • In user studies, it is rated equal to or better than commercial closed-source systems such as Dreamina and Kling.
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  43. 840AI / GeneralarXiv:2608.06301

    HarnessOpt-Bench: Evaluating LLMs at Harness Optimization

    • The authors design a trustworthy execution environment that turns harness optimization into a reproducible evaluation target.
    • They separate the model, harness, and task effects through 111 evaluations across five frontier models and four tasks.
    • Differences between optimization models are larger on average than differences between coding harnesses.
    • Broader modifications are associated with larger improvements, while detailed failure-trace analysis is not correlated with improvement.
    • A native harness is not always better than a shared harness, with 11 wins and 9 losses.
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  44. 839LLMs / NLParXiv:2608.05139

    Toward Skill-Native LLMs: Skill Entropy for Benchmarking and Training Long-Horizon Reasoning

    • It defines skill entropy as a directional metric that measures how hard it is to switch between two skills.
    • It builds Skill2-Bench, a benchmark with 558 skills across 9 domains.
    • Frontier models show monotonically decreasing accuracy on tasks with high skill entropy.
    • Skill entropy RL improves Qwen3-4B from 34.4% to 68.4% and Qwen3-1.7B from 14.6% to 40.1%.
    • It is a reusable training signal that can also be applied to existing datasets such as OpenR1-Math.
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  45. 838RoboticsarXiv:2608.06332

    GeniWorld: A Generalizable Interactive World Model for Robotic Manipulation via Visual Actions

    • We improved spatial accuracy by using a visual action representation based on URDF rendering instead of numeric actions.
    • By separating robot kinematics from environment dynamics, we reduced scene overfitting and improved generalization.
    • Autoregressive video prediction supports closed-loop interaction at 8 Hz on an H20 GPU.
    • With only limited real data, it achieves robust zero-shot generalization in OOD environments.
    • Policies trained on trajectories synthesized by the world model improve overall success from 40.8% to 69.0%.
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  46. 837Machine LearningarXiv:2608.05136

    The Loss Does Not See the Basis, but Adam Does

    • We proved that gauge equivalence, or rotational invariance, is necessary to preserve low-rank bias.
    • A classification of nine optimizers shows that GD, Muon, and Shampoo are equivalent, while Adam, RMSProp, and Lion are not.
    • In matrix sensing, equivalence classes achieve recovery error from 0.00 to 0.29, whereas coordinate-wise classes are at 0.42 to 0.57.
    • In Transformers, Adam separates gauge-equivalent initializations from the first step and creates a 56% difference.
    • Interpolating from coordinate-wise forms to equivalent scalars improves monotonically, proving that heterogeneity is the cause.
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  47. 836RoboticsarXiv:2608.05674

    JoyAI-RA 0.5: Scaling Robot Manipulation Learning via Dual Action Alignment

    • The authors built a world model, LAC-WM, that learns physical dynamics by extracting latent actions from human videos.
    • We propose an explicit alignment method that aligns human and robot trajectories in a unified physical action space.
    • With inner-loop and outer-loop reinforcement learning, it achieves both fast adaptation and improvement of the base policy.
    • As the scale of first-person human video increases, robot performance improves consistently and does not saturate.
    • It shows strong performance on both seen tasks and new variants on a real AgiBot robot.
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  48. 835AI / GeneralarXiv:2608.05466

    Recursive Synthesis for Long-Horizon Terminal Tasks

    • Recursive synthesis makes tasks progressively harder while preserving consistency across instructions, environments, answers, and verifiers.
    • After 15 rounds, it generates 37,484 tasks, and the synthesis yield does not fall, which means there is no apparent ceiling.
    • The intermediate answer length grows from 67 lines to 374 lines, which is a 5.6 times increase, and the number of instructions increases by more than 6 times.
    • When the synthetic data is used for SFT and PPO training, performance improves by up to 10 points on Terminal-Bench 2, Hard, and Long-Horizon tasks.
    • DeepSeek-V4-Pro pass@4 falls from 90 percent to 2.5 percent, which shows that the tasks become substantially harder.
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  49. 834RoboticsarXiv:2608.06375

    $ω$-0: A Latent Predictive World Action Model for Concurrent Humanoid Loco-Manipulation

    • We trained full-body cooperative motion in one model without separating locomotion and manipulation.
    • Instead of future video generation, we improved action quality using latent embedding prediction, a lightweight world model.
    • We built and released omega-HOME, a 40-hour real-home humanoid dataset.
    • It performs 11 household tasks with a single model and outperforms prior imitation learning, VLA, and WAM methods.
    • We propose a pipeline that converts human video data into robot-executable actions through simulation replay.
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  50. 833AI / GeneralarXiv:2608.05987

    AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning

    • It aligns token-level distillation gaps with environment transition points by aggregating them at the turn level.
    • It recursively updates a Bayesian belief state to compute credit that reflects the accumulated context of earlier turns.
    • On ALFWorld with Qwen2.5-7B, the method achieves an 89.1% success rate, outperforming GRPO and existing self-distillation methods.
    • The performance gap widens for longer horizons, while the effect is smaller on short tasks.
    • It is fully compatible with standard policy optimization without extra rollouts or a learned critic.
    原文摘要PDF