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Every archived paper with its summary, highest number first.

Papers 1,193Archive updated BenchmarksRSS feed
  1. 795RoboticsarXiv:2608.02304

    TRACE: Ergodic Trajectory Optimization for Active Scene Reconstruction

    • On all eight Replica scenes, it achieves higher PSNR than ActiveGS, with an average gain of 1.5 dB. The image-quality metrics LPIPS and SSIM move in the same direction.
    • The gains differ by scene type. In office scenes, SSIM is similar but LPIPS improves by 6% to 19%, while in room and hotel scenes the SSIM gap widens from 0.012 to 0.017.
    • The gain does not come simply from taking more observation frames. A variant that added 10 random shots on the ActiveGS path matched the original within 0.08 dB, while a variant with 10 evenly interpolated shots was actually 1.7 dB worse.
    • If the footstep-depletion trick is removed and only the original kernel ergodic objective is used, performance drops by 2.1 dB and becomes worse than ActiveGS on 6 of the 8 scenes. This shows that suppressing redundant observations is the key.
    • On a Unitree Go2 quadruped robot with an Intel D435 RGB-D camera, the system was run on a 42-square-meter space for a 5-minute mission, and it executed without infeasible commands at the planned horizons.
    • The real-world execution success rate was 100%, while the compared NBV baselines lightly touched the environment in every attempt. Because the trajectory itself is the optimization variable, waypoints can be passed directly to the controller.
    AbstractPDF
  2. 794AI / GeneralarXiv:2608.02365

    Faster-WAM: Do World Action Models Need Deep Action Modules?

    • The main issue is speed. Even Fast-WAM, a representative low-latency model, takes 211.7 ms for one action chunk, about three times the 71.4 ms of π0.5, a VLA model.
    • DoT removes the one-to-one alignment between video layers and action layers required by prior MoT methods, so action-head depth can be set independently of backbone depth and can access representations from all backbone layers.
    • On the average of four LIBERO task groups, it reaches 98.5%, 0.9 points above Fast-WAM's 97.6%, and gains 2.6 points on LIBERO-Long. It also outperforms π0.5, X-VLA, and Motus, all of which use robotics pretraining.
    • Under the same 24GB consumer GPU setting, latency is 66.5 ms for Faster-WAM, 68.2 ms for π0, 71.4 ms for π0.5, 105.3 ms for X-VLA, and 211.7 ms for Fast-WAM. LingBot-VA and Motus could not be measured because they ran out of memory.
    • On out-of-distribution generalization in LIBERO-Plus, it reaches 75.0% and beats Fast-WAM's 51.5% by 23.5 points. Camera perturbation rises from 16.4% to 67.9%, sensor noise from 37.7% to 82.7%, and language perturbation from 68.9% to 92.1%.
    • Ablation starts at 49.5% and climbs step by step to 60.3% with simple DoT using only the final layer, 66.8% with KV-Fusion, 71.3% with RoPE alignment, and 75.0% after removing text cross-attention. Layer mixing signals are concentrated in middle layers but spread widely across all layers.
    AbstractPDF
  3. 793RoboticsarXiv:2608.02580

    Ego2Robot: Scalable Robot Data Synthesis from Egocentric Human Data

    • The pipeline creates virtual fingertips from 21 hand keypoints using a weighted index and middle finger combination of 0.7 and 0.3, extracts the gripper center and opening width, and removes temporal noise with Savitzky-Golay filtering and SLERP. For videos without hand-pose annotations, it estimates pose with WiLoR and DynHaMR, and Qwen3.5 splits long videos into sub-tasks.
    • From about 1,940 hours of raw video, it produces 18,561 hours of data for 15 robot morphologies, which is about a 9.6x expansion.
    • In pretraining mixture experiments, the 1:1 mix ranks first in five of seven settings, reaching RoboTwin Clean 68.1% plus 5.9, Randomized 53.5% plus 2.6, and task-semantic axis 54.1% plus 7.9, while the 1:3 mix brings almost no gain.
    • By factor, lighting adds 7.6 points, robot color adds 6.4, and camera offset adds 5.9, which means visual robustness gives the largest gain. Unseen objects rise from 29.3% to 40.0% with the 3:1 mix, paraphrasing reaches 68.5%, and Franka stays below 7%, which shows transfer to robots with very different kinematics failed.
    • In the ablation that uses only egocentric data and no robot data, raw egocentric video scores 28.1%, the pipeline raises it to 31.7%, increasing the number of morphologies from 1 to 15 raises it to 33.5%, and adding the raw egocentric data raises it further to 37.3%.
    • On five long-horizon tasks with a real ARX ACone robot, the best result came from converting 20 teleoperation demonstrations into about 7-minute egocentric play videos per scene and mixing them, which improved block insertion by 14 points and screw insertion by 13 points.
    AbstractPDF
  4. 792ResearcharXiv:2607.29660

    A Simple Necessary and Sufficient Condition for Yang--Baxter Integrability

    • It gives a rigorous proof, for translation-invariant nearest-neighbor spin chains, of a conjecture that had been open for 40 years: that the Reshetikhin condition is necessary and sufficient for Yang-Baxter integrability.
    • The condition can be checked directly at the Hamiltonian level and is equivalent to conservation of the total energy flow, so it is also experimentally observable.
    • It establishes a quantum version of the Liouville-Arnold theorem, showing that Yang-Baxter integrability is equivalent to the existence of infinitely many local conservation laws.
    • When searching for new integrable models, one can check the Hamiltonian directly instead of finding an R-matrix, which greatly simplifies the search.
    AbstractPDF
  5. 791RoboticsarXiv:2607.29302

    BWM: A Low-Cost High-Fidelity World Simulator for Robot Learning

    • An action-conditioned world model predicts future scenes conditioned on robot actions, combining initial environment guidance, dynamic history, and precise action control.
    • We built a training-data pipeline that preserves action-observation alignment through trajectory replay, duplicate clip sampling, and enhanced initial observations.
    • It ranks first overall among open-source entries in WorldArena Challenge Track 1 plus two Track 2 applications.
    • The policy trained on BWM-generated data reaches a 71.00% average success rate on a real robot, a large improvement over the previous best world simulator at 53.33%.
    • We release model checkpoints, training and inference code, and data-generation and policy-evaluation interfaces as open source.
    AbstractPDF
  6. 790Computer VisionarXiv:2607.29627

    FlexComposer: Unified Video Compositing from Images to Dynamic Footage with Flexible Trajectory Control

    • It proposes a unified video synthesis framework that handles still images and dynamic videos within the same framework.
    • It standardizes heterogeneous inputs with a unified normalized foreground representation that separates the foreground's intrinsic motion from global movement.
    • It uses spatially aware latent injection based on the translation equivariance of the VAE latent space to achieve precise trajectory control without extra trainable modules.
    • It learns lighting and shadows naturally from a hybrid dataset that combines simulation, real movie footage, and generated data, together with a synthetic-to-real curriculum.
    • It outperforms prior state-of-the-art methods in visual quality, temporal consistency, and trajectory adherence.
    AbstractPDF
  7. 789LLMs / NLParXiv:2607.27497

    SkillSmith: Learning to Compose Parametric Skills and Textual Knowledge

    • It treats model weights, or prefix cache, as a modality on par with text so that the LLM can read and reason over both together.
    • SkillSmith takes source-task weights, text metadata, and a target description as input, and directly synthesizes new prefix weights tailored to the target.
    • It builds a new synthetic dataset called Composite SNI to verify the effectiveness of combining text and weights on new tasks that require composing skills from two tasks.
    • With 4B Gemma 3, it beats all baselines that use only text or only weights, and on MMLU-ProX it even beats methods based on downstream fine-tuning.
    • It shows that retrieval-based methods can synthesize weights effectively in noisy environments even without exact source mapping.
    AbstractPDF
  8. 788RoboticsarXiv:2607.29613

    WCM: A World Critic Model for Vision-Language-Action Reinforcement Learning

    • It defines the critic's fundamental limitation in VLA reinforcement learning, when it sees only a single frame, as the state approximation problem, and shows that scalar value regression cannot learn temporal structure.
    • WCM is built on a LeJEPA structure and is explicitly trained so that the critic's representations capture temporal dynamics by predicting future latent states and estimating value at the same time.
    • It is compatible with modern VLA backbones such as π0, π0.5, and OpenVLA-OFT, and can be integrated into both on-policy and off-policy training.
    • It achieves consistently best performance on 149 tasks across four benchmarks, with especially large gains in out-of-distribution generalization.
    • On seven real-robot manipulation tasks, it confirms stable performance with OpenVLA-OFT and π0.5-based off-policy RL.
    AbstractPDF
  9. 787RoboticsarXiv:2607.28993

    ST-WAM: Semantic-Temporal World Action Model for Robust Manipulation under Visual Distribution Shifts

    • The authors discover a hallucination phenomenon in which world action models hallucinate training-scene content in visually unfamiliar environments, and they confirm that 70.6 percent of 180 inspected cases exhibit this behavior.
    • They show through 290-frame triplet experiments that DINOv3 features are more robust to visual changes than VAE latent representations while still separating task states well.
    • A dual-space future expert predicts both VAE and DINO spaces, and a current-anchor intention retrieval module finds task-relevant evidence from the recent DINO history.
    • It achieves 98.7 percent on LIBERO and 92.8 percent on RoboTwin 2.0, and on visually perturbed environments such as LIBERO-Plus it improves by 21.3 points over Fast-WAM and raises real-robot success from 25.8 percent to 61.5 percent.
    • It is trained end to end without extra pretraining or task-specific annotation, and it is efficient because it does not require explicit future generation at inference time.
    AbstractPDF
  10. 786Computer VisionarXiv:2607.29679

    Scaling Properties of Text Conditioning in Visual Generation

    • The paper proves that natural-language prompts stop improving image quality as they get longer because they do not add more information, whereas structured prompts continue to add information and improve quality linearly.
    • It introduces two information measures, GPG for image-text alignment and ED for attribute precision, and uses them to quantify the relationship with diffusion loss mathematically.
    • It trains an LLM prompter with SFT, cold start, and verifier-based reinforcement learning so that short user requests are automatically converted into information-rich structured prompts.
    • The final system outperforms every open-source model evaluated on compositional, reasoning, and world-knowledge benchmarks, and it matches or exceeds closed top-tier models.
    • Fine-grained editing, such as object moves, material changes, and scene replacement, becomes possible through field edits in the structured prompt.
    AbstractPDF
  11. 785AI / GeneralarXiv:2607.28527

    MANTA: Multi-Agent Network Topology Adaptation for Self-Evolving Multi-Agent Systems

    • We propose structural self-improvement, which changes the communication topology in real time while solving the task instead of fixing it before execution.
    • It reuses structures learned from past experience before a task starts and partially revises them when problems arise during execution.
    • Across five benchmarks, it averages 74.0 points, 5.8 points higher than the previous best.
    • Rather than simply increasing the number of agents, it improves structure by rewiring connections or adding verification steps.
    • Meta-level operations, meaning structure planning and revision, account for only about 12% of total token usage, so it is efficient.
    • It also has the lowest token usage among multi-agent systems.
    AbstractPDF
  12. 784LLMs / NLParXiv:2607.28229

    EMBL AI Librarian: Life-Sciences Knowledge Layer for AI Agents

    • It builds a natural-language knowledge layer for AI agents on top of Europe PMC, which contains 40 million records.
    • A single LLM orchestrates the full pipeline, from query planning to execution, paper parsing, and evidence-snippet extraction.
    • It handles the process through live search instead of a dense vector database that would require 744 GB, which greatly reduces infrastructure cost.
    • On ScholarQA-Bench, it improves Citation F1 by 16 points, and on LitQA2 it raises accuracy from 70.3 with GPT-5.4 plus web search to 78.9 with GPT-5.4 plus the Librarian.
    • Applied to the ProClaim verification pipeline, it improves agreement with expert consensus by 5 points, and the code is public.
    AbstractPDF
  13. 783Machine LearningarXiv:2607.28022

    Flux-OPD: On-Policy Distillation with Evolving Contexts

    • It proposes Flux-OPD, which uses evolving contexts as supervision signals during training in open-ended domains.
    • Through reverse KL decomposition, it shows that the student is distilled by a geometric mean teacher and defines a conflict term.
    • The context correction strategy does not use an unstable context teacher as the direct target, and instead injects only the difference signal into a stable reference teacher.
    • It uses the conflict term as a weighting signal, correcting strongly when contexts agree and weakly when they conflict.
    • It achieves 19.63 to 20.61 on HealthBench and 81.48 to 82.26 on VBench for 1.3B, with consistent gains across three independent runs.
    AbstractPDF
  14. 782LLMs / NLParXiv:2607.27652

    Harness-G: A Graph-Structured Harness for Search Agents

    • It finds a retrieval-equivalence collapse phenomenon, where different queries increasingly retrieve the same information as training progresses.
    • It replaces free-form query generation with graph-based finite action selection over Select, Lookup, and Answer.
    • Structured Non-myopic Credit, or SNC, assigns credit by comparing alternative actions from the same state.
    • It improves average F1 by +10.74 points for Graph-R1 at 1.5B and +3.98 points at 3B, reaching the best score on six QA benchmarks.
    • Graph construction costs $0 when done programmatically, far cheaper than LLM-based graph construction, which costs $2.81 to $4.14.
    AbstractPDF
  15. 781AI / GeneralarXiv:2607.28336

    Correcting What You Cannot See: Credit Assignment for Perception Distillation in Multimodal Reasoners

    • It formalizes the credit-assignment problem in multimodal distillation by separating perception failures from reasoning failures.
    • A soft AND gate, formed by multiplying the sub-failure rate, or 1-PSR, and the teacher-student mismatch, or KL, increases correction strength only when both signals are present.
    • It separates perception and reasoning rollouts, distilling only the perception segment while leaving the reasoning segment to RL.
    • When moving from 32B to 8B, the average score across 8 benchmarks rises from 56.94 to 61.22, and when moving from 8B to 2B it rises from 44.50 to 47.28.
    • Removing the PCD weight lowers performance by 2.22 points, and removing the separated rollout lowers it by 0.88 points, confirming the ablation.
    AbstractPDF
  16. 780Machine LearningarXiv:2607.27372

    Explorative Modeling: Unlocking a Third Pretraining Axis and End-to-End Generation

    • It discovers a third scaling axis in generative model training called exploration. In addition to parameters and data, more exploration improves performance.
    • It improves FLOP efficiency by 4.1x, sample efficiency by 6.2x, and parameter efficiency by 47%. The effect grows with data size, from 7% to 36%.
    • On ImageNet 256x256, it achieves an FID of 1.43 without guidance, near state of the art.
    • It enables end-to-end generative models, where inference matches training, and gets similar performance with 16x to 256x fewer steps than diffusion.
    • It observes that as exploration increases, generalization also improves.
    AbstractPDF
  17. 779AI / GeneralarXiv:2607.28227

    Qwen-UI-Agent Technical Report: Toward Next-Generation Real-World Centric Foundation GUI Agents

    • It trains and evaluates in a real-device mobile environment with more than 100 actual smartphones and more than 150 apps.
    • It uses a mixed GUI and CLI action space, which allows multiple actions to be batched in one turn, and more than 40% of the actions are batched.
    • It uses online RL for tasks that run for more than 100 turns, and it accelerates rollouts with 10,000 concurrent environments.
    • It reaches 82.1% on MobileWorld, 79.5% on OSWorld-Verified, and 73.6% on WebArena.
    • It supports notification-driven spontaneous service starts and cross-platform workflows between mobile and PC.
    AbstractPDF
  18. 778Computer VisionarXiv:2607.28627

    ReToken: One Token to Improve Vision-Language Models for Visual Retrieval

    • It finds that the attention, or query-key, scores of a VLM are poorly suited for visual information retrieval, with an average recall@1 of only 5.1 percent.
    • It adds a single learnable token, ReToken, to explicitly retrieve relevant frames in value space.
    • On Visual Haystacks, it improves Qwen3VL-8B by 13.4 points and InternVL3.5 by 12.4 points, which is more than a 20 percent relative gain.
    • Even when trained only on image QA, it transfers zero-shot to long videos and improves LVBench by 8.0 points.
    • It is lightweight enough to train and run on a single H100, and the added retrieval latency is about 0.4 seconds.
    AbstractPDF
  19. 777Computer VisionarXiv:2607.27380

    VideoCoCo: Code-as-CoT for Physically-Consistent Video Generation via an Agentic Dual-Engine System

    • It uses executable code, Blender, as a process-level thought chain to preserve physical consistency.
    • The code agent programs the scene and a simulator renders a draft video, and then the video editor composes the final result realistically.
    • It builds the VideoCoCo-3K dataset, which contains draft, instruction, and target pairs, for editor adaptation training.
    • It improves PhyGenBench from 0.475 to 0.558 and VBench-2.0 from 52.18% to 77.88%, which are both state of the art.
    • Without tuning, the draft alone improves from 0.475 to 0.506, and LoRA tuning raises it further to 0.558, which separates the contribution of each stage.
    AbstractPDF
  20. 776Computer VisionarXiv:2607.28625

    ACE-Data-0: Human-Centric Ambient Capture as Embodied Data Engine

    • The ACE system turns real home environments into recording studios, with both table-scale and room-scale settings.
    • It is a large dataset with 150 hours, 17 million frames, 75,000 episodes, 50 participants, and 200 task categories.
    • It synchronizes egocentric and multi-view exocentric video, full-body and hand motion, 6-DoF object trajectories, audio, and touch at the millisecond level.
    • It supports zero-shot motion transfer from simulation to the real world and exposes the limitations of more than 30 existing methods through benchmarking.
    • It can serve as training data for imitation learning, world models, and VLA systems.
    AbstractPDF
  21. 775Computer VisionarXiv:2607.28611

    Chimera: Designing and Chinchilla-Scaling Hybrid Visual Diffusion Transformers

    • It designs a hybrid diffusion backbone that combines KDA, linear attention, MLA, global attention, and unit consistency for local patterns.
    • Without positional embeddings, it processes text, images, and video in a single stream using only raster order.
    • HeteroP automatically transfers hyperparameters across scales based on functional fan-in for each tensor.
    • With 11B total parameters and 2B active parameters, it achieves 7.3x better compute efficiency than Wan-2.1 at 2B, and training takes about 600 H100 days.
    • It is trained on 5-second clips and can extrapolate to 30-second video generation in zero shot, with only a 6 percent FID drop at 6x longer length.
    AbstractPDF
  22. 774LLMs / NLParXiv:2607.26760

    Metis: Memory Foundation Model

    • It is the first prototype to implement native memory state inside model weights instead of relying on external RAG.
    • Memory storage, forgetting, and updating are all performed automatically by the model's forward pass, which enables gradient-free updates.
    • It introduces a hyper-memory block and a local memory block inspired by Fast Weight Programming.
    • At 128K context, it is 2.4 to 2.67 times faster than full context and uses 67 times less storage per session, 16.79 MB versus 1118 MB.
    • Its limitation is compression loss on long tasks, and in some cases it also causes semantic confusion.
    AbstractPDF
  23. 773ResearcharXiv:2607.27197

    The Maxwell Conjecture is False

    • It refutes Maxwell's 1873 conjecture that the number of critical points of n point charges is at most (n-1)^2 by giving a counterexample with 5 charges.
    • A configuration of an equilateral triangle plus two axial charges produces 21 critical points around the center, for a total of 24 nondegenerate critical points.
    • By iterating the construction, 3 plus 2m charges can produce at least 4 plus 20m critical points, which is a ratio of 10 critical points per charge.
    • The idea was suggested by GPT-5.6 Sol and then verified in Mathematica and Maple before the authors wrote the mathematical proof.
    • The construction improves the previous best ratio of 25/7, or about 3.57, to 10.
    AbstractPDF
  24. 772Computer VisionarXiv:2607.28624

    PhiZero: A World Model Built Around Physical Language

    • We discovered a discrete compressed representation called a physics language in natural videos through self-supervised learning. It symbolizes patterns of state change rather than pixels.
    • A reasoning-then-rendering architecture explicitly predicts physical evolution and then synthesizes video with a diffusion model.
    • It substantially improves physical consistency over prior world models on video generation and physics-understanding benchmarks.
    • It demonstrates zero-shot action transfer by transferring human motions to humanoid robots or robotic hands.
    • It learns physical common sense from video data alone, without physics laws or simulators.
    AbstractPDF
  25. 771Computer VisionarXiv:2607.28595

    Beacon: Knowing When and How to Perform Agentic Visual Reasoning

    • It defines two problems in tool use: Mode Adaptiveness, knowing when a tool is needed, and Tool Effect, knowing whether the tool actually helps.
    • Existing models gained little from tools because the gains on hard problems were offset by errors on easy ones.
    • The Necessity-Aware Adaptive Reward (NAAR) reduces unnecessary tool calls, and Hint-Conditioned Exploration (HCE) strengthens tool use on hard problems.
    • It achieves a best-in-class average of 58.98% across 13 benchmarks, a +6.07-point gain over the base model, and the largest tool gain-minus-gap improvement at +3.14 points.
    • It recovers about 40% of the all-wrong group through hint rollouts, effectively reusing the learning signal.
    AbstractPDF
  26. 770AI / GeneralarXiv:2607.28272

    MemHarness: Memory Is Reconstructed, Not Replayed

    • It applies the human memory principle of reconstruction to AI agents, meaning that past experiences are critically revised and edited to fit the current situation.
    • It uses GRPO reinforcement learning to let the agent learn memory reconstruction naturally, without extra human labeling.
    • It achieves 85.2 percent on ALFWorld and 75.6 percent on WebShop, outperforming both pure RL and static-memory methods.
    • When the memory reconstruction module is removed, performance drops to 74.6 percent, confirming that reconstruction itself is the key contribution.
    • Learning to reconstruct memory during training also improves base reasoning ability at inference time even without memory, which the paper calls a latent guidance effect.
    AbstractPDF
  27. 769AI / GeneralarXiv:2607.27191

    Can AI agents conduct open-ended AI research? Early evidence from two case studies

    • It proposes a new method called shadow evaluation, in which AI is given the research questions from unpublished papers and the original authors do the grading.
    • The same failure patterns were reproduced for both Opus 4.8 and GPT-5.6 Sol.
    • The agents were good at engineering work, such as coding and running experiments, but they lacked strong judgment and creativity in research design.
    • The five core failures were weak research-level standards, failure to back away from dead ends, poor resource awareness, non-creative responses to feedback, and drift away from the instructions.
    • They used less than half of the API budget, still had time left over, and never received a pass in AI self-review.
    AbstractPDF
  28. 768LLMs / NLParXiv:2607.28568

    Frontis-MA1: Training an AI4AI Model towards Recursive Self-Improvement in Machine Learning Engineering

    • It builds the full-stack OpenMLE system for AI-for-AI improvement and creates 5,758 executable task environments.
    • It trains the meta-evolution agent Frontis-MA1-35B by using reinforcement learning to learn four evolutionary operators: drafting, improving, debugging, and crossover.
    • On MLE-Bench Lite, the medal average rises from 39.4 percent to 71.2 percent, which beats GPT-5.5 plus Codex at 68.2 percent.
    • A separate benchmark, NatureBench Lite, also confirms transfer learning, which shows that the result is not simple benchmark memorization.
    • It releases the model weights, task environments, and training code, providing a reproducible basis for recursive self-improvement research.
    AbstractPDF
  29. 767LLMs / NLParXiv:2607.25852

    AngelSpec: Towards Real-World High Performance Inference with Speculative Decoding

    • The authors empirically show that the best drafter depends on the task type in real service settings, such as chat versus code or math.
    • DFly improves block diffusion quality with a hybrid target-conditioned backbone and an autoregressive head conditioned on predecessors.
    • D-cut treats verification as a shared batch-level resource and adjusts verification depth adaptively based on load.
    • On Hy3-A21B, it increases average accepted length by about 30 percent and achieves the highest throughput across concurrency levels from 4 to 64.
    • It records 1.98 to 2.40 times the throughput of autoregressive decoding and 10.5 to 11.8 percent higher throughput than DFlash.
    AbstractPDF
  30. 766RoboticsarXiv:2607.26991

    RL$^2$-VLA: Adaptive RL Latent Compositional Steering with Test-Time Scaling for Vision-Language-Action Models

    • It applies compositional steering with a lightweight RL policy learned on top of VLA latent representations.
    • It finds different scaling laws in success and failure states, and the optimal intervention is to act only on failures.
    • It integrates a SAFE failure detector so that RL steering is activated only when failure is predicted.
    • It achieves up to a 17.3% success-rate improvement on the out-of-distribution SIMPLER and PolaRiS benchmarks.
    • It also transfers to the real PiperX robot, where it improves success by 17.5%.
    AbstractPDF
  31. 765Computer VisionarXiv:2607.27110

    FreqForcing: Autoregressive Long Video Generation via Spectral Self-Anchoring

    • It analyzes autoregressive video-generation error accumulation in the frequency domain and shows that low-frequency energy drift is the root cause.
    • Spectral Self-Anchoring, or SSA, fuses low-frequency content from the first frame with high-frequency content from the most recent frame to preserve both stability and dynamics.
    • Without extra training, it can extend a 5-second training model to stable generation for up to 2 minutes, which is a 24x horizon expansion.
    • On the VBench-Long metric, it outperforms previous training-free methods and remains competitive with training-based methods.
    • It adds only about 16.5% inference-latency overhead over Self-Forcing.
    AbstractPDF
  32. 764Computer VisionarXiv:2607.27194

    VidMap: Exploiting Temporal Structure for Video-Based Structure-from-Motion

    • We combine SLAM's sequential tracking with SfM's global optimization to overcome the limitations of both.
    • We use temporal order as the primary signal to prevent visual aliasing errors.
    • We add a monocular depth prior to global optimization to address scale drift and degenerate configurations.
    • We outperform existing SLAM, SfM, and learning-based methods by a wide margin on a challenging dataset with extreme motion.
    AbstractPDF
  33. 763AgentsarXiv:2607.26598

    Living-Harness Is an Interactive-Agent Evolver

    • It turns agent failures into persistent procedural repair rather than one-off lessons and stores them permanently.
    • It accumulates knowledge along two axes: episode memory, including trigger conditions, failure patterns, and recovery actions, and a state graph, including state nodes, repair edges, and transition rules.
    • It structures what to change and how to change it through a domain-level guide called Evolution-SOP.
    • It improves by an average of 10.07 points on tau^2-Bench and 9.91 points on MultiWOZ-2.4.
    • Even when the harness state evolved by GPT-5.2 is transferred to other models such as Gemini 3 Pro, GLM-5, and Qwen3-max through retrieval alone, performance still improves.
    AbstractPDF
  34. 762RoboticsarXiv:2607.26055

    $π\mathbf{R}^2$: Reactive Real-time Flow Policies

    • It separates joint-state information in a fast channel and vision-language information in a slow channel asynchronously, so it can respond in real time without waiting for vision latency.
    • A latency-adaptive noise schedule ensures smooth behavior regardless of hardware delay.
    • On a real robot based on GR00T-N1.7, with xArm6 plus XHand, it achieves 25 Hz recomputation, about 4x faster than before.
    • On contact-rich real-world tasks such as organizing books, inserting boxes, and catching falling objects, it improves success by up to 30%.
    • It can be applied with only minimal changes to existing VLA architectures, which makes it practical.
    AbstractPDF
  35. 761Machine LearningarXiv:2607.26246

    Weak-to-Strong On-Policy Distillation

    • It proposes a new distillation method that separates the capability direction from the logit difference of a weak model pair, positive and negative, and adds it to a strong student.
    • It proposes three settings: RL before and after model pairs, model pairs of different sizes, and answer versus hint pairs.
    • Across four math and three coding benchmarks, it achieves relative improvements over existing OPD of 11.4% in math and 12.0% in coding.
    • Even though it learns only from weak models, it outperforms the actual weak teacher models themselves.
    • It found that the RL-based pair and the size-based pair provide complementary signals, strengthening the reasoning skeleton and the solution procedure, respectively.
    AbstractPDF
  36. 760ResearcharXiv:2607.27011

    Qwen-Audio-3.0-Gen-Preview Technical Report

    • It unifies speech, music, sound effects, and mixtures into a single generation path with a non-autoregressive structure.
    • It designs a shared VAE that compresses 48 kHz stereo into a 25 Hz latent representation.
    • It uses only about 10 percent of the music data used by specialized prior models, yet still leads in 3 of the 7 SongBench tasks.
    • On Seed-TTS-Eval, it achieves the highest speaker similarity, and it also outperforms Seed-Audio-1.0 in multi-speaker consistency.
    AbstractPDF
  37. 759Computer VisionarXiv:2607.27180

    HumanCLAW: Can Vision-Language Models Act Through a Body?

    • We built a framework that separates behavioral decision making in VLMs from motor control, so it evaluates reasoning ability alone.
    • We built HumanCLAW-Bench, consisting of 1,218 egocentric exploration and interaction episodes across 41 indoor scenes.
    • Even the best of nine state-of-the-art VLMs reaches only a 16.8% success rate.
    • The core failure is not perception but a lack of self-awareness, meaning the model does not know where its body is, whether it has arrived, or whether it has collided.
    • The conclusion is that current VLMs only describe the world; they do not feel the body they control.
    AbstractPDF
  38. 758ResearcharXiv:2607.27199

    Settling the Optimal Exponent Relating Sumsets and Difference Sets

    • It is the first proof that the optimal universal exponent relating sumsets and difference sets is exactly 2.
    • An AI agent, Hyra, discovered the key structure through 24 hours of autonomous search, and a human then verified it rigorously in mathematics.
    • It also provides a Lean 4 formal proof, which makes the result machine-checkable.
    • The core construction combines base-12 digit techniques, a three-state carry automaton, and the Chinese remainder theorem.
    AbstractPDF
  39. 757Computer VisionarXiv:2607.27205

    TurboVLA: Real-Time Vision-Language-Action Model at 32 Hz on an RTX 4090 with <1 GB VRAM

    • It proposes a direct vision-plus-language-to-action approach by removing the LLM-centered architecture.
    • With 0.2B parameters, the model is only 6% the size of π0.5, which has about 3.4B parameters, yet matches or exceeds its performance with a 97.7% success rate on LIBERO.
    • It achieves 32 Hz inference, 31.2 ms, on a consumer RTX 4090 and uses less than 1 GB of VRAM, making edge deployment possible.
    • It outperforms π0.5 consistently on four real-robot tasks with an AgileX Piper.
    • Removing language drops the success rate to 70.8%, proving that language conditioning is still important even at the execution level.
    AbstractPDF
  40. 756RoboticsarXiv:2607.25918

    DC-WAM: Dynamic-Centric Visual Supervision and Reasoning for World-Action Models

    • We redistribute visual supervision from appearance reconstruction to interaction-centered dynamics in RGB video prediction, so PSNR drops but policy performance improves.
    • A temporal difference flow-matching objective and tracker-guided weighting focus on robot-object contact motion rather than static background and texture.
    • The DynaRoute attention bias predicts dynamic-relevant tokens and steers visual attention toward them.
    • On LIBERO-Plus OOD, it reaches 60.9%, a 9.4-point gain over FastWAM, and reduces the ID-OOD gap from 46.1 to 37.2.
    • On real robot bimanual tasks, it achieves up to a 30-point improvement under lighting changes.
    AbstractPDF
  41. 755Computer VisionarXiv:2607.25537

    Visual prompt engineering for video models

    • It systematically proposes and validates visual prompt engineering, or VIPE, for video models.
    • On Veo 3.1, converting sketches into photorealistic scenes raises physical reasoning accuracy from 41.3 percent to 59.3 percent, a gain of 18 points.
    • It uncovers a realism bias, meaning video models reason much better on realistic scenes, so abstract benchmarks underestimate their ability.
    • VIPE is often more effective than test-time scaling methods such as text-prompt optimization or self-consistency.
    • Stepwise experiments confirm that scene consistency improves whenever realism is increased by one level.
    AbstractPDF
  42. 754Computer VisionarXiv:2607.25389

    HOME: Robust Hough-space Matching Method for Structured and Textureless Videos

    • It uses the maxima in Hough space as keypoints to achieve stable feature-point detection on straight-line structures.
    • A one-dimensional radial descriptor mathematically guarantees rotation and translation invariance, which removes the need for separate orientation estimation.
    • It is training-free and outperforms line-segment matching methods based on LSD and LBD on every metric.
    • It runs in real time at 16.5 ms total, and although the Hough transform takes most of the time, 7.7 ms, it can be improved with GPU acceleration.
    • It remains robust even on nearly textureless images with alpha equal to 0.95, finding more than 400 inlier matches.
    AbstractPDF
  43. 753Computer VisionarXiv:2607.26004

    Parallel Decoding Distillation for Fast Image and Video Generation

    • It trained a 'parallel decoder' that predicts multiple denoising steps in parallel from a single forward pass, speeding up generation.
    • It solved the mode collapse problem by using only a simple regression objective, without VSD or GAN losses.
    • On Wan2.1 14B text-to-video, it ranked first on VBench and outperformed the baseline on diversity metrics.
    • On Qwen-Image 20B, it achieved SOTA scores with only 4 to 8 NFE.
    • On the LTX-2.3 22B model, it can generate a 720p 10-second video with audio in only 8 NFE.
    AbstractPDF
  44. 752Computer VisionarXiv:2607.24904

    Mage-VL: An Efficient Codec-Native Streaming Multimodal Foundation Model

    • The codec-native tokenizer, Mage-ViT, reduces visual tokens by more than 75% by encoding only dynamic patches.
    • Trained from scratch on just 560M unlabeled images, it matches SigLIP2, which uses billions of image-text pairs, demonstrating pretraining data efficiency.
    • A dual-system design, with a System 1 event gate for fast reaction and a System 2 causal decoder for deep reasoning, supports real-time streaming understanding.
    • With 4B parameters, it matches Qwen3-VL-4B on static tasks and surpasses even the 15B Phi-4 model on video understanding and 2D/3D spatial reasoning.
    • It achieves up to 3.5x inference speedup and reports seven key empirical findings.
    AbstractPDF
  45. 751RoboticsarXiv:2607.26056

    INTACT: Isomorphic Intent-to-Action Learning for Search-Free World Models

    • It integrates a JEPA prediction architecture with an intent-to-action interface so it can generate actions directly without retrieval.
    • It processes physical next states and target intents with one shared predictor, while designing the endpoint gradient asymmetrically.
    • In direct mode without retrieval, it reaches 85.78% to 100% success on four LeWM tasks, with 2.9 to 5.5 ms inference time.
    • With selective CEM search, it achieves 96.86% macro success while using 23.44x fewer samples than pure CEM.
    • Joint training with a single encoder over four tasks yields 89.39% direct macro success.
    AbstractPDF
  46. 750LLMs / NLParXiv:2607.26057

    Pass the Baton: Trajectory-Relayed On-Policy Distillation

    • It discovers a teacher-student asymmetry in direction switching: the teacher reflects and changes direction, but the student just keeps going straight.
    • It experimentally confirms that even 0.35% teacher tokens can raise accuracy by +7.23 points.
    • It limits the number and length of teacher interventions through a relay budget so the student policy does not drift too far away.
    • It cuts the average training trajectory length by more than 50%, from 4,658 to 2,296 tokens, while still improving accuracy.
    • A single regression objective makes stable training possible without VSD or GAN losses.
    AbstractPDF
  47. 749Computer VisionarXiv:2607.25948

    MODUS: Decoder-Only Any-to-Any Modeling of Diverse Modalities

    • It is the first model to process 15 modalities with a single decoder, without modality-specific heads, losses, or task pipelines.
    • Unlike prior any-to-any models, it uses a large pretrained decoder, BAGEL, to greatly reduce training cost.
    • Uniform timestep sampling avoids modality confusion and enables stable training.
    • It naturally supports chained generation, converting through intermediate modalities, and cross-modal self-verification, checking its own outputs through other modalities.
    • It releases the 29-million-sample MODUS-DATASET and two checkpoints as open source.
    AbstractPDF
  48. 748RoboticsarXiv:2607.25895

    HiFi-UMI: Learning Deployable Manipulation Policies from High-Fidelity UMI Data Alone

    • It overcomes the fidelity limits of prior UMI data with 3 mm trajectory accuracy, microsecond synchronization, and a field of view of about 200 degrees.
    • Zero-robot post-training means it is trained only on HiFi-UMI data and can then be deployed directly on real robots, which is validated on all three backbones.
    • The strongest policy reaches an 85 percent success rate on precision insertion tasks, even though HiFi-UMI contains no trajectories for those tasks.
    • With 4,000 hours of pretraining, action error decreases by 41 percent on 10 unsupported tasks, and one backbone gains an additional 18.1 percentage points in success rate.
    • HiFi-UMI-2K, a high-quality dataset containing 2,000 hours of microsecond-synchronized data, was released as open source.
    AbstractPDF
  49. 747ResearcharXiv:2607.24136

    How to read Mathematics? A study guide

    • Mathematics should be read in terms of time per page, not pages per time, so read slowly, very slowly.
    • When reading a definition, you must create examples and counterexamples. A definition without examples, and an example without a definition, is not enough.
    • When reading a proof, first skim it, then follow it line by line, and finally summarize the key idea.
    • Practice problems should be looked at as early as possible, and the problem itself should be read like a mathematical text.
    • It warns against overreliance on AI, citing EEG research that suggests heavy dependence on LLMs weakens brain connectivity.
    AbstractPDF
  50. 746Software EngineeringarXiv:2607.25886

    RSIBench-Data: Benchmarking Data-Centric Research for Recursive Self-Improvement

    • It is the first benchmark that fixes the training, evaluation, and serving infrastructure so that only pure data research ability is measured.
    • Among four frontier agents, including Codex and Claude Code, none is consistently dominant. Codex wins on non-SWE tasks, while three SWE tasks are won by different agents.
    • After feedback, 78% of runs still drop in score on the final attempt even after reaching a peak, which reveals a discovery-reliability gap.
    • Increasing max effort helps find stronger candidates earlier, but it reduces the number of exploration attempts, which reveals a depth-width trade-off.
    • A self-improvement experiment on the same model family, kimi-k2.6, improves from 8% to 21% but still falls short of the original 33%.
    AbstractPDF