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Every archived paper with its summary, highest number first.

Papers 1,193Archive updated BenchmarksRSS feed
  1. 645RoboticsarXiv:2607.15065

    DriftWorld: Fast World Modeling through Drifting

    • It is the first application of a drifting generative model, which generates in a single forward pass, to a robotic world model and achieves 30-plus fps, 17 times faster than diffusion.
    • It proposes three adaptations: an action-conditioned drifting field, a loss in the DINOv2 feature space, and frame-wise action conditioning.
    • On Push-T, it raises IoU from 0.635 to 0.781, which greatly improves action-exploration quality, and it shows consistent gains across 5 environments.
    • It is also valid as an offline policy-evaluation metric and achieves a Pearson correlation of 0.92 to 0.99 with actual performance.
    • It proves that fast and accurate imagination directly helps both robot planning and policy evaluation.
    AbstractPDF
  2. 644AI / GeneralarXiv:2607.13285

    Harness Handbook: Making Evolving Agent Harnesses Readable,Navigable, and Editable

    • It defines the behavior localization problem as finding where in the code a behavior described by a change request is implemented.
    • Harness Handbook reorganizes code by behavior rather than by file or function, and links each behavior directly to source code.
    • Behavior-Guided Progressive Disclosure (BGPD) guides exploration step by step from high-level behavior descriptions to relevant implementation details.
    • On two open-source harnesses, Codex and Terminus-2, it outperforms the baselines with win rates of 38.3% and 45.6%, compared with 28.3% and 26.7%.
    • It delivers especially large gains on multi-file edits, rarely executed code paths, and interactions across modules.
    AbstractPDF
  3. 643RoboticsarXiv:2607.15163

    Scaling Behavior Foundation Model for Humanoid Robots

    • It adopts motion tracking as a unified training paradigm, combining diverse whole-body control problems under reference-motion imitation.
    • Experiments show that the synergy between the quantity of on-policy rollouts and the diversity of reference motions is the key to scaling.
    • It introduces the Humanoid Transformer, a scalable architecture in which structured action representations emerge naturally.
    • Compared with existing humanoid controllers, it reduces MPKPE by 10% locally and 82% globally, and reaches an overall accuracy of 0.9836.
    • It is validated in both simulation and on real robots, confirming its practicality.
    AbstractPDF
  4. 642Computer VisionarXiv:2607.15038

    Video = World + Event Stream

    • It proposes a decomposition in which video equals world plus event stream, and uses it to define a general-purpose pretraining task.
    • It adds free-form actions, expressed as natural-language instructions in parentheses, to the event stream so that the agent can perform open-vocabulary actions rather than a fixed action set.
    • It maintains real-time duplex audio-visual dialogue with 200 ms model response latency and 550 ms end-to-end interaction latency.
    • It demonstrates flexible transfer from pretraining to downstream tasks, suggesting that the same streaming logic can adapt to a range of real-time problems.
    AbstractPDF
  5. 641RoboticsarXiv:2607.15275

    RoboTTT: Context Scaling for Robot Policies

    • It integrates Test-Time Training, fast weights, into a VLA model to achieve an 8K time-step context without increasing inference latency.
    • With just one human video demonstration, it gains one-shot imitation of a new behavior, succeeding in 6 out of 10 cases while all baselines fail.
    • DAgger Distillation enables real-time performance improvement, reaching 83% success under disturbances, compared with a previous best of 53%.
    • The overall task success rate improves by 87% over the single-step baseline, and it completes a 5-minute, 10-step assembly all the way through, which no previous model could do.
    • Expanding context length from 1K to 8K yields 62% better performance, showing that context is a scaling axis for robots as well.
    AbstractPDF
  6. 640Computer VisionarXiv:2607.14935

    VideoChat3: Fully Open Video MLLM for Efficient and Generalist Video Understanding

    • The 3D vision Transformer, I3D-ViT, processes frames as spatiotemporal bundles rather than separate images, which greatly reduces token count.
    • An adaptive mechanism that adjusts frame resolution by importance improves efficiency for real-time streaming.
    • It releases 3 million high-quality multimodal instruction examples, including Academic2M, LV116K, and OL617K.
    • With a 4B model, it outperforms previous open-source models of the same class on 12 benchmarks, including MotionBench, VideoMME, and LVBench.
    • It releases the model, code, training strategy, dataset, and pipeline, which provides a reproducible foundation.
    AbstractPDF
  7. 639LLMs / NLParXiv:2607.14777

    SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning

    • It creates a self-evolving loop that automatically extracts natural-language skills from completed trajectories and feeds them back into the policy.
    • It converts action-probability changes with and without a skill into a dense token-level supervision signal, OPD, and combines that with RL.
    • On ALFWorld, it reaches 91.8 percent, which is 7.4 points better than the previous best, and it beats GRPO trained on the full dataset even when using only 40 percent of the data.
    • It generalizes strongly to unseen environments, confirming that it learns real strategy rather than memorization.
    • It improves performance using only the trained policy, without extra prompts or external memory at inference time.
    AbstractPDF
  8. 638Computer VisionarXiv:2607.13639

    OvisOCR2 Technical Report

    • Given a document-page image, it generates text, equations, tables, and visual regions as Markdown in a natural reading order in one pass.
    • It combines real document annotations with synthetic pages whose images and ground truth are generated from the same HTML, applying supervised learning, reinforcement learning, on-policy distillation, and model merging.
    • The 0.8B model achieved the highest scores on both benchmarks: 96.58 on OmniDocBench v1.6 and an Avg3 of 75.06 on PureDocBench.
    • By outperforming pipeline approaches with a single small model, it suggests the possibility of simplifying deployment configurations for document-retrieval and knowledge-building systems.
    • However, validation is limited to two public benchmarks and an in-house benchmark for converting page images to Markdown, so it does not guarantee performance in other document-processing workflows.
    AbstractPDF
  9. 637Computer VisionarXiv:2607.12450

    Let RGB Be the Language of Vision

    • It converts structured visual information such as masks, depth maps, and poses into RGB images, representing multiple tasks as a common RGB-to-RGB editing problem.
    • A single general-purpose image-editing backbone handles understanding tasks and conditional-generation tasks without adding task-specific encoders or decoders.
    • It showed competitive zero-shot performance without separate fine-tuning on tasks including segmentation, depth estimation, and pose-conditioned generation.
    • By connecting visual understanding and generation through the same input-output format, it can serve as a common interface for general-purpose vision-language systems.
    • A limitation is that performance depends heavily on a web-scale-trained image-editing backbone, and gaps remain relative to dedicated models on some specialized tasks.
    AbstractPDF
  10. 636AI / GeneralarXiv:2607.13034

    Do AI Agents Know When a Task Is Simple? Toward Complexity-Aware Reasoning and Execution

    • It defined ACRR to measure unnecessary agent work and proposed E3, which estimates scope, executes the minimal path, and expands only when verification fails.
    • Across 121 edits in MSE-Bench's controlled simulator, it maintained the same 100% success rate as the strongest baseline.
    • E3 reduced cost by 85%, tokens by 91%, and files inspected by 92%, and was also 16% better than the adaptive-search baseline.
    • It can be applied as an execution policy that reduces the waste of reading an entire repository for a small change while retaining verification as a safeguard.
    • The main figures come from a controlled simulator rather than a deployed agent, and real-model validation was limited to a gpt-4o case study, so broader validation is needed.
    AbstractPDF
  11. 635Computer VisionarXiv:2607.14076

    From Pixels to States: Rethinking Interactive World Models as Game Engines

    • It views interactive world models through the action-state-observation loop of traditional game engines and classifies related research along four dimensions.
    • The four dimensions are player-action control, game-state transitions, long-term persistence of states and observations, and real-time generation.
    • It presented a data engine that collects more than 90 hours of gameplay in Black Myth: Wukong together with frame-level actions, actual game states, and visual observations.
    • The framework and data can serve as reference material for research on next-generation generative game engines that track explicit states beyond predicting screen images.
    • The paper focuses on classifying methods and collecting data; it does not present a complete world model that simultaneously satisfies rule adherence, persistence of long-term consequences, and real-time generation.
    AbstractPDF
  12. 634Computer VisionarXiv:2607.14088

    VideoRAE: Taming Video Foundation Models for Generative Modeling via Representation Autoencoders

    • It compresses features from multiple layers of a frozen video foundation encoder through a lightweight one-dimensional self-attention projector.
    • The continuous latent representations can be used in a diffusion Transformer, while discrete tokens produced through multi-codebook quantization can be used in an autoregressive model.
    • On UCF-101, the autoregressive and diffusion generators achieved gFVD scores of 40 and 93, respectively, and converged approximately 5× faster than competing autoencoders.
    • It can accelerate video-generation training by converting semantically rich frozen video representations into a reconstructable generative latent space.
    • Generation results were verified primarily on UCF-101 and in controlled 2B-scale text-to-video experiments, leaving validation on larger models and more varied video domains outstanding.
    AbstractPDF
  13. 633Machine LearningarXiv:2607.13988

    TRACE: Turn-level Reward Assignment via Credit Estimation for Long-Horizon Agents

    • At each state on a tool-call boundary, it computes the log-probability of the correct answer using a frozen reference model, then uses temporal differences in log-ratio state values as per-action rewards.
    • Without a separate critic or process labels, it provides finer-grained turn-level credit than outcome rewards, and values cancel across unnecessary tool calls.
    • On BrowseComp-Plus, Qwen3-4B improved from 7.2 to 35.6, and Qwen3-30B-A3B from 8.4 to 42.6.
    • By teaching useful intermediate actions in long search processes, it accelerated convergence in pure reinforcement learning and transferred behavior to public-web evaluations.
    • Current value estimation is tailored to search with short, known correct answers, so whether the same log-probability criterion is suitable for long code patches or open-ended answers remains unverified.
    AbstractPDF
  14. 632Computer VisionarXiv:2607.13125

    Boogu-Image-0.1: Boosting Open-Source Unified Multimodal Understanding and Generation

    • It developed four models, Base, Turbo, Edit, and Edit-Turbo, combining a stronger multimodal encoder with agentic prompt rewriting.
    • It jointly refined data quality, training procedures, and inference-time scaling so that improvements in understanding translated into image generation and editing.
    • Using 208.62 million unique images and a theoretical base-model training cost of approximately $400,000, it matched or exceeded several public models on benchmarks.
    • It released weights, code, and training methods under Apache 2.0, allowing reuse in unified image-generation and editing research with smaller budgets.
    • Although it approached leading closed systems, it did not report outperforming them overall, and the comparisons are limited to the scope of published standard evaluations.
    AbstractPDF
  15. 631LLMs / NLParXiv:2607.12395

    Ring-Zero: Scaling Zero RL to a Trillion Parameters for Emergent Reasoning

    • It stabilized zero reinforcement learning for a 1-trillion-parameter model by combining importance-ratio clipping, training-inference ratio correction, and mixed-precision control.
    • Scaling the model to 1 trillion parameters increased sample efficiency and the performance ceiling, with training progressing from a discovery phase to a refinement phase.
    • Behaviors such as structured formatting, self-verification, parallel reasoning, and context anxiety emerged without explicit rules.
    • It proposed a framework for evaluating reasoning processes through understandability, reproducibility, and efficiency as well as accuracy, providing a tool for analyzing large-scale reasoning training.
    • Empirical evidence is concentrated on seven math benchmarks and a 1-trillion-parameter configuration, leaving open whether the results generalize directly to other tasks and smaller computation budgets.
    AbstractPDF
  16. 630RoboticsarXiv:2607.13960

    GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch

    • It uses an action-centric WAM that jointly learns future visual changes and actions during training, but decodes only actions at inference without generating video.
    • It combines AC-WM and WAM pretraining and assigns visual changes and action generation to separate Transformer experts.
    • In a local RTX 4090 environment, it reduced action-inference latency to 85 milliseconds while retaining the policy-learning benefits of future visual supervision.
    • Agent-based AutoResearch automates the search over training configurations, potentially reducing the development burden of real-time closed-loop robot control.
    • The 85-millisecond figure was measured in a specific RTX 4090 deployment environment, so it does not guarantee latency or performance on other hardware or a wider variety of robot tasks.
    AbstractPDF
  17. 629Computer VisionarXiv:2607.12800

    UniVR: Thinking in Visual Space for Unified Visual Reasoning

    • VR-GRPO combines overall outcome rewards with rewards for error-prone steps to learn logical and physical consistency in visual reasoning.
    • It built the VR-X benchmark from 16 sources, bringing long-horizon manipulation, spatial puzzles, and physical reasoning together in a purely visual format.
    • UniVR improved by up to 25% on VR-X, and stronger visual reasoning also improved performance on multiple multimodal-understanding benchmarks.
    • It released code, data, and models for research on planning and reasoning in visual space without textual explanations.
    • The 34B model and long-video training require substantial computational resources, and step rewards also depend on a general-purpose VLM that lacks detailed physical knowledge.
    AbstractPDF
  18. 628RoboticsarXiv:2607.12992

    ChunkFlow: Towards Continuity-Consistent Chunked Policy Learning

    • It divides each action chunk into fixed, revisable, and future regions and deterministically blends the overlapping portions during execution.
    • It trains with seam loss and first- and second-order continuity losses, using history corruption, scheduled sampling, and AWAC fine-tuning to improve robustness to execution errors.
    • In CALVIN, LIBERO, and real-robot experiments, it improved the balance between success rate and motion stability while maintaining low inference latency.
    • By reducing jitter at chunk boundaries during training rather than through post-processing filters, it can be applied to real-time VLA robot control.
    • The theoretical result that seam discrepancies decrease with overlap length holds under mild smoothness assumptions, and the experiments are limited to three environments.
    AbstractPDF
  19. 627RoboticsarXiv:2607.13033

    DenseReward: Dense Reward Learning via Failure Synthesis for Robotic Manipulation

    • It automatically synthesized trajectories of physical failures such as collisions, failed grasps, dropped objects, and recovery in simulation without human labels.
    • It trained a dense reward model that takes visual observations and language instructions and scores task progress at every frame.
    • In both simulated and real manipulation, it predicted dense rewards more accurately than general-purpose VLMs and existing robot reward models.
    • These rewards provide fine-grained progress signals for model-predictive control and reinforcement learning and can be used to improve robot policies after imitation learning.
    • The current scope focuses on relatively short manipulation tasks, leaving tool use, long-horizon work, and rewards reflecting human preferences for future work.
    AbstractPDF
  20. 626LLMs / NLParXiv:2607.13027

    PalmClaw: A Native On-Device Agent Framework for Mobile Phones

    • It implements an open-source framework that directly manages sessions, memory, skills, tools, and the agent loop on a smartphone.
    • It reduces lengthy screen interactions by exposing device capabilities as tools with explicit arguments, structured results, and clear execution boundaries.
    • On MobileTask, its task success rate was 11.5% higher in relative terms than the strongest baseline, and completion time was 94.9% shorter.
    • It can run mobile automation close to user data and sensors on the device while controlling permissions and task scope at the tool level.
    • The results were verified on a specific Android device and MobileTask configuration, and using new device capabilities requires separately providing safe, dedicated tools and permission boundaries.
    AbstractPDF
  21. 625AI / GeneralarXiv:2607.12463

    Function-Aware Fill-in-the-Middle as Mid-Training for Coding Agent Foundation Models

    • It designed function-aware FIM mid-training by exploiting the similarity between a coding agent's action-observation-continuation structure and the function call-return structure.
    • It selected functions to mask using program-dependence graphs and criteria for complexity and inferability, then trained on 2.6 billion tokens from 968 GitHub repositories.
    • SWE-Bench-Verified improved by 2.8, 3.0, and 3.2 points on Qwen-family 7B, 14B, and 8B models, respectively.
    • The effect persisted across multiple agent post-training procedures and also mitigated capability degradation in general coding and non-coding tool use.
    • The training corpus and evaluations focused on Python, and generating reasoning explanations depended on a teacher model, so other languages and teacher-free settings remain unverified.
    AbstractPDF
  22. 624Computer VisionarXiv:2607.12477

    Self in Space: Benchmarking Self-Awareness and Spatial Cognition in UAV Embodied Intelligence

    • SIS-Bench evaluates UAV self-spatial understanding by crossing the two axes of spatial cognition and self-awareness with three stages: perception, memory, and reasoning.
    • Through expert validation, it created 13 tasks and 4,856 question-answer pairs from 1,646 real UAV videos.
    • Current MLLMs were weaker at self-awareness than spatial cognition, and their performance declined progressively from perception to memory and reasoning.
    • SIS-Motion, which combines optical flow and visual features, improved perception and memory along both axes, as well as downstream UAV decision-making performance.
    • The evaluation mainly remains at the level of video-based question answering and does not cover agents that integrate multiple sensors to carry out actual long-duration autonomous flight.
    AbstractPDF
  23. 623Computer VisionarXiv:2607.12592

    WanToFight: Real-Time Generative Game Engine for Multi-Player Combat Interaction

    • It implemented streaming autoregressive generation by adding block-causal attention and a rolling KV cache to the Wan-1.3B video diffusion model.
    • A player-binding module associates each keyboard input with a character, and a local causal key-input module is trained through a staged curriculum.
    • A 4-step DMD-distilled model and a pruned VAE generated 512×384 video at 30 frames per second throughout a complete match on a single RTX 5090.
    • It shows the potential to handle adversarial interaction between two players and complex physics together in one real-time generative engine.
    • It is currently limited to a single game, KOF '97, with remaining issues in fine visual detail for complex actions and accumulated error in sessions longer than 2 minutes.
    AbstractPDF
  24. 622ResearcharXiv:2607.12264

    Heisenberg Scaling in Many-Body Kinetic Uncertainty Relation via Quantum Feedback

    • It analyzes the counting precision of a superradiant spin ensemble using a many-body kinetic uncertainty relation and mean-field equations incorporating feedback.
    • Feedback after quantum jumps sustains transient superradiant activity, maintaining collective effects that depend on the particle number N.
    • Both analytical results and numerical simulations showed Heisenberg-like scaling, with the variance of the counting observable decreasing as 1/N².
    • It proposes a design principle for turning collective dissipation from noise into a resource for precision in quantum clocks and counting devices.
    • The results are based on theory and numerical simulations of a specific superradiant spin model, and no experimental validation on actual devices was presented.
    AbstractPDF
  25. 621ResearcharXiv:2607.12772

    Quantum States Prepared by Wormholes: Long-Wavelength Deviations from Bunch-Davies

    • It calculated the quantum states of tensor perturbations and test-scalar-field perturbations prepared when a wineglass-shaped wormhole creates a new universe.
    • It showed that the Bunch-Davies vacuum is reproduced at short wavelengths, while wormhole-charge-dependent deviations arise at long wavelengths.
    • These deviations leave small shifts and oscillations in the angular power spectrum produced by the subsequent inflation.
    • As the charge approaches 0, scalar and tensor deviations are suppressed, providing evidence that the topological transition to a no-boundary instanton is smooth.
    • These are cosmological predictions derived from idealized wormhole geometry and test-field calculations, not detections of signals in actual observational data.
    AbstractPDF
  26. 620Computer VisionarXiv:2607.12894

    Hy-Embodied-VLM-1.0: Efficient Physical-World Agents

    • It organizes pretraining and post-training data around a three-stage capability taxonomy: understanding action-relevant states, reasoning about action transitions, and sequential and adaptive reasoning.
    • It limits active parameters to 3B using the Hy3-A3B language backbone, the Hy-ViT2 visual encoder, and a mixture-of-experts architecture.
    • It achieved the highest performance among similarly sized models on 19 of 38 benchmarks and improved by an average of 8.4% over the previous Hy-Embodied-0.5.
    • By supporting multi-turn interaction and long-horizon reasoning with few active parameters, it can serve as a foundation model for latency-sensitive physical-world agents.
    • The model with 3B active parameters only approaches the previous generation's model with 32B active parameters rather than surpassing it on every evaluation, and its top performance was limited to half of the 38 benchmarks.
    AbstractPDF
  27. 619RoboticsarXiv:2607.13017

    FlowWAM: Optical Flow as a Unified Action Representation for World Action Models

    • It represented actions as optical flow in the same format as RGB video and jointly diffusion-modeled RGB and flow within a shared video generator.
    • In policy mode, it predicts actions from flow; in world-model mode, target flow guides future video. Videos without action labels are also used for pretraining.
    • Success rates on RoboTwin were 92.94% for Clean and 92.14% for Random, and it achieved an EWMScore of 63.71 on WorldArena.
    • By connecting robot policies and future-scene prediction through a single video-friendly representation, it can use motion information from large-scale general video for control learning.
    • The reported figures come from RoboTwin, WorldArena, and a limited set of real-robot tasks, so generality across other robot embodiments and contact-centered tasks requires further validation.
    AbstractPDF
  28. 618AI / GeneralarXiv:2607.11185

    SCALECUA: Scaling Computer Use Agents with Verifiable Task Synthesis and Efficient Online RL

    • VeriGen automatically synthesizes verifiable computer-use tasks through Docker interactions and feedback from multiple agents.
    • It uses Frontier Sampling, which allocates executions to each task's current capability frontier, and Visual Context Segmentation, which processes only recent screens in separate segments.
    • It created more than 24,000 verifiable tasks and approximately 3,000 high-quality reinforcement-learning tasks, increasing training speed by 2.83 times.
    • ScaleCUA scored 68.7% on OSWorld and 54.0% on ScienceBoard and can be used for large-scale online training of open computer-use agents.
    • Each execution is limited to 50 turns, and evaluation centers on the Ubuntu desktop, so generalization to very long-horizon tasks and Windows or macOS has not been verified.
    AbstractPDF
  29. 617ResearcharXiv:2607.11762

    Operational Concealment of Measurement Incompatibility by Quantum Channels

    • It defined operational concealment as the phenomenon in which measurement incompatibility can no longer be certified when all input states pass through a quantum channel.
    • In finite dimensions, it proved that some binary POVM pair is exactly concealed only if the channel's Heisenberg adjoint has a nontrivial kernel.
    • It distinguished injective dynamics that only contract from cases where information rank decreases, and derived an exact expression for concealment robustness in qubit channels.
    • Using quotient-space duality, it interpreted this robustness as an experimentally accessible reward advantage in channel-output games.
    • The analysis is limited to finite-dimensional, finite-outcome measurements, exact knowledge of the channel, complete input tomography, and single use; it does not cover adaptive multiple uses.
    AbstractPDF
  30. 616ResearcharXiv:2607.11637

    From Circuits to Hardware: Benchmarking Standard and Qubit-Efficient Quantum Optimization on Real Hardware

    • It compares VQE, CVaR-VQE, several QAOA variants, PCE, and QRAO using the same real-hardware procedure for four NP-hard problems.
    • On IBM Heron r1 and r2, it records transpiled circuit sizes, execution results, and gate-count-based fidelity estimates for 247 method-problem combinations.
    • At intermediate Heron-r2 error rates, noise began to dominate execution around F_est≈0.1, corresponding to approximately 770 two-qubit gates.
    • No method produced a feasible solution for QAP, and qubit-efficient methods also ran larger problems only within the fidelity budget.
    • The conclusions concern the tested implementations and hardware; they do not prove asymptotic inferiority of the entire QAOA family or quantum advantage over classical methods.
    AbstractPDF
  31. 615Computer VisionarXiv:2607.09450

    Robustifying Vision-Language Models via Test-Time Prompt Adaptation

    • RITA reduces adversarial outliers by using optimal transport to align the feature distribution of multiple augmentations of a test image with the text-prototype distribution.
    • A dynamic cache accumulates reliable cues from the test stream to continually refine prompts online.
    • Across multiple experiments, it improved VLM robustness to adversarial attacks without reducing clean-image accuracy.
    • Because it requires neither model retraining nor access to labeled data, it can be added to existing CLIP-family classification systems as a test-time defense.
    • Current validation centers on image classification. Extension to generative tasks such as image captioning remains open, and the method does not eliminate every risk from new attacks.
    AbstractPDF
  32. 614Computer VisionarXiv:2607.11588

    FoundationGeo: Learning Spatial Pixel-Wise Fields for Monocular Metric Geometry

    • Stage 1 uses DINOv3 initialization and a 10.2M multi-domain corpus to learn affine-invariant, high-quality relative geometry.
    • Stage 2 applies a pixel-wise scale field and ray-direction correction field for spatially varying metric correction.
    • We identify focal-length distribution mismatch as the key bottleneck in zero-shot metric generalization.
    • By augmenting with Blender-based synthetic data over a wider range of focal lengths, we improve robustness to intrinsics shift.
    • Across seven zero-shot benchmarks, it leads by an average of more than 5.2%, preventing large drops across domains.
    • It outperforms existing metric methods such as DepthPro, Metric3D V2, UniDepthV2, and MoGe-2.
    AbstractPDF
  33. 613Computer VisionarXiv:2607.11862

    Evidence-Backed Video Question Answering

    • E-VQA defines a new task that requires three outputs: an answer, temporal evidence, and spatial evidence in the form of masklets.
    • ST-Evidence is the first human-verified spatiotemporal grounding benchmark, with both generative and objective variants.
    • It shows that state-of-the-art models such as Qwen3-VL, OpenAI-o3, and Gemini-2.5-Pro have decoupled QA accuracy and grounding ability.
    • An automatically generated 160K-scale ST-Evidence-Instruct pipeline bridges the gap between reasoning and grounding.
    • Fine-tuning UniPixel 7B brings large gains, with t-mean up by 27.2 and J&F up by 13.8.
    • Scaling alone is not enough; the task requires fundamental advances in architecture, data, and training objectives.
    AbstractPDF
  34. 612ResearcharXiv:2607.11738

    Qwen-Audio-VAE Technical Report

    • We reduce downstream DiT training cost by using a 12.5 Hz low-bit-rate latent space.
    • An asymmetric encoder-decoder with latency-aware encoder pruning encodes 32 minutes in 541 ms, a 3.62x speedup.
    • Trained on 5 million hours of multi-domain audio, it generalizes across speech, music, and sound-effect domains.
    • Four discriminators, multi-period, multi-resolution STFT, multi-scale STFT, and sub-band CQT, provide adversarial supervision.
    • It achieves robust reconstruction quality across domains on the public LibriSpeech, AudioCaps, and SongDescriber benchmarks.
    • It outperforms the previous best VAE on STFT distance, SI-SDR, and related metrics.
    AbstractPDF
  35. 611LLMs / NLParXiv:2607.11487

    LightMem-Ego: Your AI Memory for Everyday Life

    • It aligns visual and audio streams on a timeline and segments them into events.
    • It incrementally organizes memory into three levels: current, short-term, and long-term.
    • It designs a memory router that dynamically routes queries according to their temporal scope and intent.
    • It demonstrates four core scenarios: finding objects, recalling conversations, daily summaries, and routine discovery.
    • It uses a lightweight architecture that can be deployed on smartphones and AI glasses.
    • It outperforms baselines without explicit multimodal memory in retrieval, QA, and latency.
    AbstractPDF
  36. 610Computer VisionarXiv:2607.11562

    MonkeyOCRv2: A Visual-Text Foundation Model for Document AI

    • It builds MonkeyDoc v2, the largest document image pretraining corpus, with 113 million images across 17 languages.
    • It jointly learns image-to-text generation and pixel-level document reconstruction to achieve character-level visual recognition.
    • It improves CRNN recognition accuracy from 58.7 percent to 67.3 percent, and the 110M UniMERNet-T outperforms the 325M UniMERNet-B.
    • A 0.7B document parsing model achieves open-source SOTA on MDPBench, beating the previous best, the 3B dots.mocr, by 2.8 percent.
    • With the vision encoder frozen, it achieves stronger document understanding than large VLMs such as Qwen3-VL-235B and GPT-5.2.
    • It shows consistent gains over CLIP, DINO, and SAM across 8 benchmarks.
    AbstractPDF
  37. 609RoboticsarXiv:2607.11689

    From World Action Models to Embodied Brains: A Roadmap for Open-World Physical Intelligence

    • It defines WAMs as action-conditioned predictive models and classifies them as models that explicitly predict the results of interventions.
    • It identifies three structural limitations: the model-representation gap, the objective-standardization gap, and the ecosystem-system gap.
    • An embodied brain is the central model that integrates physical observations and compares interventions to output intended state transitions.
    • A physical harness converts the brain's outputs into execution tools and controllers, validates them, and records traces.
    • Shared contracts make heterogeneous data, tasks, and components mutually comparable.
    • Closed-loop post-training turns verified interactions into reusable experience.
    AbstractPDF
  38. 608Computer VisionarXiv:2607.11886

    Read It Back: Pretrained MLLMs Are Zero-Shot Reward Models for Text-to-Image Generation

    • We use image-conditioned prompt log-likelihood from an MLLM as the reward, which removes the need for preference labels and fine-tuning.
    • Self-SpectraReward is a closed-loop self-improvement scheme in which the understanding branch of a unified model (UMM) evaluates its own generation branch.
    • We validate it at scale with 9 MLLM backbones from 4B to 235B, 4 model families, 3 RL algorithms, and 2 diffusion models.
    • A larger reward MLLM is not always better; reward-policy alignment matters more than reward-model size.
    • Self-SpectraReward outperforms a 235B external model, demonstrating the importance of policy-reward alignment.
    • It consistently outperforms prior MLLM-derived reward methods on five OOD text-to-image benchmarks.
    AbstractPDF
  39. 607LLMs / NLParXiv:2607.11881

    Metacognition in LLMs: Foundations, Progress, and Opportunities

    • It defines metacognition as a loop of monitoring, meaning state evaluation, and control, meaning strategy adjustment, and uses that lens to classify LLM research.
    • It organizes a range of metacognitive measurement methods, including psychology-based, neurofeedback-based, confidence-based, and interpretability-based approaches.
    • The literature shows that metacognition helps reduce hallucination, improve reliability, and improve human-AI collaboration.
    • It analyzes metacognitive applications in reasoning models, agents, multi-agent systems, memory, retrieval, and tool use.
    • It systematically studies the effects of model size, post-training, and sampling temperature on metacognitive ability.
    • It identifies gaps in metacognitive evaluation and proposes future research directions, including domain generality and architecture design.
    AbstractPDF
  40. 606ResearcharXiv:2607.11699

    Qwen-Music Technical Report

    • It compresses an entire song into a 25 Hz single-codebook Music Semantic Token so the LLM has room to predict the sequence.
    • The Melody-CoT mechanism plans melody as an explicit intermediate representation before token generation, which improves musicality and structural consistency.
    • Qwen-Music-Render combines a semantic-conditioned DiT, Spec-VAE, and Band-Mode Refiner to synthesize 48 kHz stereo audio.
    • It is trained on more than 5 million hours of multilingual music data with a curriculum by quality level and multi-stage post-training from SFT to DPO to GSPO.
    • In blind tests by expert evaluators, it achieves a 66.7% win rate against MiniMax 2.6 and a 58.3% win rate against Mureka V8.
    • It reaches state of the art on 13 of 16 objective metrics, including SongBench, SongEval, and AudioBox-Aesthetic.
    AbstractPDF
  41. 605Machine LearningarXiv:2607.11505

    Proxy Exploration and Reusable Guidance: A Modular LLM Post-Training Paradigm via Proxy-Guided Update Signals

    • By separating exploration, meaning proxy, and alignment, meaning primary, it restructures post-training into a modular, asynchronous, and reusable pipeline.
    • It supports weak-to-strong improvement by transferring relative improvement signals rather than the absolute distribution of the proxy.
    • Exploration signals from a small proxy such as Qwen3-1.7B improve the larger primary model in a controllable way.
    • Compared with GRPO, OPD-based distribution alignment converges within 70 steps, which demonstrates exploration efficiency.
    • The transfer strength can be controlled with a scaling factor, which gives robust performance across different settings.
    • Caching, reuse, and cross-model transfer of exploration signals greatly improve cost efficiency.
    AbstractPDF
  42. 604Computer VisionarXiv:2607.11673

    ABot-3DWorld 0: A Universal World Model to Explore Any 3D Space

    • SGP, a single representation combining panorama and point cloud, processes all input modalities in a unified way.
    • We improve 3D consistency in a 14B VACE-based panorama video generator through 3DRL post-training.
    • Rich inputs, including multi-view and video, are handled through a strict geometric pipeline to minimize hallucination.
    • An iterative FLUX-based repair module resolves panorama resolution limits and view mismatches.
    • The ABot-3DGS data engine generates fully aligned training data of RGB, point maps, and poses.
    • It supports indoor, outdoor, and aerial domains in a single pipeline and can be linked with mapped POIs.
    AbstractPDF
  43. 603RoboticsarXiv:2607.11643

    Xiaomi-Robotics-U0: Unified Embodied Synthesis with World Foundation Model

    • It trains five generative tasks, image generation, editing, embodied scene generation, transfer, and video generation, in a single 38B-parameter autoregressive framework.
    • A structured control format separates workspace, background, foreground, target, and lighting as independent control dimensions to preserve multi-view consistency.
    • GPT-Image-2.0 ranks first in human evaluation for embodied scene generation and transfer, and first in World Arena embodied video generation.
    • Synthetic data generated by the model improves the OOD success rate of the π0.5 robot policy from 36.9% to 63.2%, a 26.3-point gain.
    • Sequential world modeling turns static scene generation into a scalable trajectory generation engine.
    • It releases code and checkpoints so the model can serve as an embodied-intelligence data engine.
    AbstractPDF
  44. 602Computer VisionarXiv:2607.09544

    The Count Is There, but Misaligned: Understanding and Correcting Counting Failures in VLMs

    • It trains three probes over VLM internal activations to predict ground-truth count, output count, and whether an error is present, and the probes reliably predict mistakes.
    • SVCCA shows that the correct-count probe and the output-count probe share a partial subspace, but the readout is misaligned, which is a structural cause of error.
    • Causal steering confirms the cause, because strengthening the count direction improves counting performance while random directions hurt it.
    • Detector-guided self-correction applies selective reprompting only when an error is predicted, which improves accuracy by up to 15.6 absolute points.
    • It improves performance through inference-time intervention alone, so the method requires no parameter update and is practical to deploy.
    • There is a strong positive correlation, with rho equal to 0.803, between probe F1 and correction gains, which means that internal error-signal quality determines correction effectiveness.
    AbstractPDF
  45. 601RoboticsarXiv:2607.09648

    B-spline Policy: Accelerating Manipulation Policies via B-spline Action Representations

    • It replaces discrete action chunking with continuous B-spline curves to accelerate execution through temporal rescaling.
    • Inference-time segment alignment ensures smooth transitions between adjacent B-spline segments, which is essential for high-speed execution.
    • On Table Cleaning, 4x speed reduces completion time by 50%, from 23.57 seconds to 11.80 seconds, while maintaining success rate at 13 out of 20 versus 14 out of 20.
    • It integrates as a drop-in replacement for both Diffusion Policy and ACT backbones, requiring minimal pipeline changes.
    • On PushT simulation, 4x speed improves the score from 0.59 to 0.73 and reduces time from 7.19 seconds to 2.87 seconds.
    • It automatically allocates control granularity to match task stages, from fast reaching to precise grasping.
    AbstractPDF
  46. 600AI / GeneralarXiv:2607.08964

    Long-Horizon-Terminal-Bench: Testing the Limits of Agents on Long-Horizon Terminal Tasks with Dense Reward-Based Grading

    • It covers 46 long-horizon terminal tasks across 9 categories, including experimental reproduction, software engineering, multimodal analysis, games, and scientific computation.
    • It uses subtask-based dense rewards that provide partial scores instead of a binary pass or fail signal, making intermediate progress visible.
    • The benchmark averages 9.9 million tokens, 231 episodes, and 85.3 minutes per task, making it far more demanding than Terminal-Bench or SWE-Bench.
    • Even the strongest model, GPT-5.5, reaches only 15.2 percent pass@1 at R ≥ 0.95, while the average pass@1 is just 4.3 percent, leaving substantial room for improvement.
    • The main failure mode is partial progress followed by timeouts and early false finishes, with weak self-verification emerging as the key bottleneck.
    • Dense rewards are essential for exposing false-finish patterns, whereas binary evaluation misses them.
    AbstractPDF
  47. 599Computer VisionarXiv:2607.09225

    Glob3R: Global Structure-from-Motion with 3D Foundation Models

    • It uses a frozen 3D foundation model, Pi3X, plus a lightweight dense matching head to turn feed-forward prediction into an optimizable geometric constraint.
    • A keyframe-based sliding-window strategy propagates tracks and relative poses across windows, which overcomes chunk-level limits.
    • On KITTI, it achieves a mean RMSE of 13.21 m, which is a 10 to 50 percent improvement over recent methods such as Scal3R at 14.55 and LoGeR at 18.65.
    • On ETH3D, it achieves R@1 of 91.58 and T@1 of 73.27, which substantially improves rotation and translation accuracy over the latest learning-based SfM method, AMB3R.
    • It improves PSNR by 2 to 3 dB in NeRF and neural rendering, which demonstrates the practical value of refined poses.
    • Global motion averaging and bundle adjustment resolve scale mismatch and recover dense geometry.
    AbstractPDF
  48. 598Machine LearningarXiv:2607.08940

    TSRouter: Dynamic Modality-Model Selection for Time Series Reasoning

    • It is the first modality plus model co-routing framework that uses the complementarity of LLMs for numerical accuracy and VLMs for global patterns.
    • It models complex interactions with a heterogeneous graph over task, query, modality, and model and uses a GNN.
    • On four time-series reasoning tasks, it achieves 16% to 46% relative improvements and builds a Pareto frontier with cost-aware optimization.
    • It greatly outperforms the best baseline even when using only 10% of the training data, showing data efficiency.
    • GPT-5 is used for node profiling and initialization from pretrained embeddings, enabling zero-shot generalization to new models and tasks.
    • The user-defined alpha parameter allows real-time control of the performance-cost tradeoff.
    AbstractPDF
  49. 597ResearcharXiv:2607.09627

    A complete ultrametric on von Neumann's incomplete tensor products

    • We define a convergence-index-based pseudo-ultrametric d on the equivalence classes Gamma of infinite tensor products, and prove the strong triangle inequality and completeness.
    • We define a gauge-invariant variant d~, which is insensitive to topological changes and consistent with von Neumann weak equivalence.
    • We prove that the product of any unitary U with 1 not in sigma(U) moves every equivalence class to the maximum distance 1.
    • We interpret d~ as a decoherence exponent: when monitoring N environmental degrees of freedom, the branching rate decays as exp(-N d~).
    • In the Everett many-worlds model, the universe is an infinite qubit tensor product, worlds are sectors, and time is a discrete unitary step.
    • The hierarchical structure of the ultrametric space corresponds exactly to the leaf space of the branching tree.
    AbstractPDF
  50. 596Computer VisionarXiv:2607.09661

    PanoWorld: Real-World Panoramic Generation

    • It uses the rotational equivariance of panoramic ERP to treat rotation as a geometric transform and model only translation explicitly.
    • DPRC uses dense conditioning on panorama rays for accurate motion control, and GMA uses geometry-aware memory augmentation to maintain scene persistence.
    • The World360 dataset contains 70K real UAV clips and 50K AirSim360 simulation clips, including multi-altitude flight trajectories.
    • Compared with Matrix-3D, it improves FID from 27.64 to 16.93 at 720p, and it improves PSNR trajectory accuracy by 2 to 4 points.
    • Real-time generation with causal forcing produces 161 frames in 8 seconds on a single H20 GPU, versus about 4 minutes and 48 seconds previously.
    • A three-stage training pipeline progressively optimizes modeling, memory, and control.
    AbstractPDF