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Every archived paper with its summary, highest number first.

Papers 1,193Archive updated BenchmarksRSS feed
  1. 695AI / GeneralarXiv:2607.20253

    Pushing the Frontier of Full-Song Generation: Hierarchical Autoregressive Planning Meets Flow-Matching Rendering

    • It supports three tasks in one framework: lyric-to-song generation, instrumental generation, and cover-song generation.
    • It reaches an Elo score of 1,129 on the Artificial Analysis Music with Vocals leaderboard, which ties for second to third place and is only 12 points behind first.
    • In tests on 500 songs across 8 genres and 5 languages, it gets the top score on 15 of 18 metrics.
    • FullDiT performs flow matching on the entire song rather than on chunks, which preserves global consistency.
    • Post-training with DPO, GRPO, and OPD improves both musicality and rendering quality.
    AbstractPDF
  2. 694LLMs / NLParXiv:2607.20145

    SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD

    • On Ascend SuperPOD, it raises the MFU for DeepSeek-V4-Pro, a 1.6T model, from 11.67 percent to 34.22 percent, or 2.93x.
    • It uses an agent called AuraKernel to automatically optimize complex NPU kernels.
    • It builds a 10K high-quality CPT plus SFT dataset specialized for operations research and applies solver verification.
    • The specialized model achieves an average zero-shot Pass@1 of 71.81 percent, which is 3.98 percentage points above GPT-5.4-Mini and 11.27 points above the base model.
    • It provides a full-stack practical example of training a trillion-parameter model on NPU hardware rather than GPUs.
    AbstractPDF
  3. 693Machine LearningarXiv:2607.19331

    ISO: An RLVR-Native Optimization Stack

    • It discovers and validates a 'spectrum inheritance' phenomenon in which the weight spectrum changes very little after RLVR training.
    • ISO-Optimizer matches AdamW accuracy on Qwen3-8B using 2.7 times fewer steps, from 270 down to 100.
    • ISO-Merger merges expert models using checkpoints alone, without data, rollouts, or gradients.
    • Freezing the spectrum alone does not improve performance much, which means both frames need to remain trainable.
    • It shows consistent efficiency gains on reasoning and coding tasks at scales from 1.5B to 8B.
    AbstractPDF
  4. 692RoboticsarXiv:2607.20033

    Robots Acquire Manipulation Skills in Seconds from a Single Human Video

    • We learn a new skill from a single human video in an average of 29 seconds, which is 507x faster than before.
    • It reaches a 62% average success rate, 45 points above zero-shot and even higher than the baseline fine-tuned with 50 robot demonstrations.
    • It preserves existing skills at 100% because it does not modify the learned parameters, eliminating catastrophic forgetting.
    • It resolves temporal misalignment and state mismatch by aligning video and robot trajectories through a task-progress manifold.
    • It implements inference-time adaptation through a causal cascade of localization, future observation prediction, and action generation.
    AbstractPDF
  5. 691Machine LearningarXiv:2607.18433

    Intelligence from Learnable Novelty

    • It shows that the three forms of intelligence, data compression, universal computation, and adaptive behavior, all come from a single principle: maximizing learnable novelty.
    • It recovers the Turing-complete rule 110 as the top one-dimensional cellular automaton, which is consistent with decades of complexity-ranking research.
    • It achieves representation learning that separates MNIST digit classes without labels, which is fully unsupervised.
    • When used as intrinsic reward in reinforcement learning, it improves performance in 9 of 10 environments.
    • It has a mathematical structure that avoids both the noisy-TV trap and the dark-room trap.
    AbstractPDF
  6. 690Computer VisionarXiv:2607.20368

    Self Gradient Forcing: Native Long Video Extrapolation

    • It defines and resolves the past-context gradient gap, which is the critical weakness of Self Forcing.
    • A two-pass strategy, where the first pass records rollouts and the second pass reconstructs gradients in parallel, solves the problem without extra memory overhead.
    • Using only 5-second training videos, it can generate coherent long videos up to 240 seconds, improving subject identity, background consistency, and temporal stability.
    • A user study found that it was preferred over Self Forcing in every setting, with GSB ranging from 29.6% to 48.7%.
    • Peak memory increases by only 8 GB, and training slowdown is about 13%, which makes the method practical.
    AbstractPDF
  7. 689Computer VisionarXiv:2607.19228

    IGGT4D: Streaming 4D Instance-Grounded Geometry Transformer

    • It is the first streaming transformer that predicts geometry and instance identity at the same time from a video stream.
    • It processes frames sequentially with causal masking and reuses past context through a KV cache.
    • The authors build InsScene4D-147K, a 4D instance-annotated dataset with 147,000 scenes.
    • It shows much stronger performance on object tracking, with T-mIoU of 58.84 compared with 45.38 for SAM2.
    • Streaming clustering keeps memory constant at 0.7 GB regardless of the number of frames.
    AbstractPDF
  8. 688LLMs / NLParXiv:2607.18235

    Automated Discovery Has No Universally Superior Harness

    • It decomposed the harnesses of OpenEvolve and TTT-Discover into components and compared 30 combinations.
    • It performed statistically significant comparisons using more than 3.1 million LLM rollouts.
    • It found a generalization problem: no fixed harness is consistently best for every model-task pair.
    • It proposed an adaptive budget allocation strategy that exploits the fact that early progress strongly predicts final performance.
    • It released all execution logs and baseline distributions as reusable statistical infrastructure.
    AbstractPDF
  9. 687Computer VisionarXiv:2607.19120

    Latent Riemannian Flow Matching for Geometry-Grounded 3D Foundation Models

    • The authors discover that VGGT tokens live on the product manifold of four hyperspheres and apply Riemannian flow matching.
    • Compared with Euclidean flow matching, the Riemannian version improves consistently on every metric, with about a 44 percent gain in LPIPS.
    • It works with a single input view and is order invariant.
    • It reduces Chamfer distance by more than 40 percent on ScanNet++ and by more than 70 percent on ETH3D.
    • The generated 3D scans align with the context frame and enable consistent scene reconstruction.
    AbstractPDF
  10. 686Computer VisionarXiv:2607.19339

    OmniReasoner: Thinking with Long Audio-Video via Native Tool Use

    • We propose a tool-use learning framework that adaptively invokes zoom-in tools for long audio-video inputs.
    • TimeAnchor preserves temporal coordinate consistency across different sampling resolutions.
    • Temporal Augmented Data Engine automatically creates training data without manual effort.
    • Accuracy improves from 29.3 to 34.8 on OmniVideoBench and from 32.0 to 35.4 on LVOmniBench.
    • As videos get longer, the zoom-in tool is called more often, and we confirm that this contributes to improved accuracy.
    AbstractPDF
  11. 685RoboticsarXiv:2607.18840

    WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory

    • It proposes a reasoning-augmented memory structure that combines short-term visual memory and long-term event memory.
    • It builds a dataset of 5 million event segments called ManipEvent-5M.
    • It can control robots through multiple modalities, including text, goal images, and demonstration videos.
    • On a real dual-arm robot, it achieves 75 percent success on long-horizon tasks and 86.7 percent on fine-grained commands.
    • Its generalization to new object categories improves sharply compared with prior work, from 43.3 percent to 60 percent OOD.
    AbstractPDF
  12. 684RoboticsarXiv:2607.19190

    Agentic Real2Sim: Physics-based World Modeling with Vision-Language Agents

    • It proposes a four-agent pipeline that automatically converts real robot manipulation recordings into MuJoCo simulation episodes.
    • A 31B open VLM achieves a similar conversion success rate, 48 out of 100, at only 3 percent of GPT-5.4's cost.
    • It handles deformable objects such as cloth and rope, as well as humanoid motion, in the same framework, not just rigid manipulation.
    • It shows that narrowing the VLM's role makes backend model replacement easier and more cost-efficient.
    • The converted episode twins can be used for robot policy training and evaluation.
    AbstractPDF
  13. 683Computer VisionarXiv:2607.19191

    ABot-World-0: Infinite Interactive World Rollout on a Single Desktop GPU

    • It builds a multi-source data pipeline that combines AAA games, simulations, and internet video.
    • It uses LongForcing to correct autoregressive drift in long rollouts with a teacher model.
    • On a single RTX 5090, it runs in real time at 720P and 16 FPS with 1.2 seconds of latency and about 19 GiB of VRAM.
    • It shows competitive control performance and consistent world evolution on WorldRoamBench.
    • On 60-second rollout evaluation, it accumulates far less visual error than Causal-Forcing.
    AbstractPDF
  14. 682Computer VisionarXiv:2607.19064

    Mage-Flow: An Efficient Native-Resolution Foundation Model for Image Generation and Editing

    • At 4B parameters, it matches the performance of competitors in the 6B to 80B range while keeping memory usage down to about 18 GB.
    • The Mage-VAE tokenizer reduces compute per pixel by 12 to 22 times compared with conventional VAEs.
    • CUDA kernel fusion improves training throughput by about 2.5 times.
    • The turbo variant generates a 1024 by 1024 image in 0.59 seconds and performs editing in 1.02 seconds on a single A100.
    • Its cost efficiency makes it a practical open baseline for researchers who want to experiment and iterate.
    AbstractPDF
  15. 681Computer VisionarXiv:2607.19343

    Masked Visual Actions for Unified World Modeling

    • It proposes an interface called Masked Visual Actions, which represents robot actions as pixel-level masks.
    • A single checkpoint supports both forward simulation and inverse action generation.
    • It works well across different environments and robot embodiments even when it is fine-tuned on only 15 hours of real robot data.
    • The imagined rollouts from the video model are strongly correlated with real execution, with r = 0.982.
    • Given a desired object motion, the inverse model synthesizes the corresponding robot action and reaches a 90 percent success rate.
    AbstractPDF
  16. 680Software EngineeringarXiv:2607.18161

    TRIM: Reducing AI-Generated CodeSlop via Agent Trajectory Minimization

    • It defines CodeSlop by functional behavior rather than surface style, such as verbosity or duplication. Any edit that can be removed while tests still pass counts as CodeSlop.
    • The cause is the agent's search process. Once it finds a correct solution, there is no reason to roll back the attempted edits, so they remain in the final patch.
    • Telling the agent to reduce its own patch was unstable. In 3.8% to 44.9% of cases it broke behavior or made the patch larger.
    • TRIM uses the task history, not the final patch, as the search space because the temporal order of edits approximates dependency structure.
    • It uses a hierarchical approach that tries to remove large chunks first, confirms success, and then splits more finely if needed, so the verification cost is about half that of Delta Debugging.
    • On SWE-Bench-Verified, it preserved correctness on 327 of 330 successful patches, or 99.1%, and 18 patches became exactly identical to the human-written reference patch.
    AbstractPDF
  17. 679Distributed SystemsarXiv:2607.18002

    ExpertPlex: A High-Goodput Disaggregated Serving System for MoE LLMs with Adaptive Persistent Kernels

    • More than 95 percent of the weights in an MoE language model belong to the expert part, so duplicating the full model for both prefill and decode wastes a lot of memory.
    • The key idea is a hybrid structure in which the heavy experts are shared across the two stages, while less than 5 percent attention is assigned separate GPUs by stage.
    • An adaptive persistent kernel, or APK, schedules at matrix-multiplication tile boundaries so that urgent decode work can take over and free capacity can return to prefill without relaunching kernels or involving the CPU.
    • A one-way MoE communication path initiated from the attention side removes MoE polling, avoids deadlock and cross-stage network interference, and overlaps communication in one stage with computation in the other.
    • In serving experiments with MiniMax-M2.7 and GLM-5.1-FP8, effective throughput rises by 5.65 times over chunked prefill, by 2.01 times over instance-level separation, and by 1.66 times over Green Context colocation.
    • Because the preemption interval depends on tile execution time rather than token count, even a prefill with thousands of input tokens can be interrupted by short decode jobs.
    AbstractPDF
  18. 678RoboticsarXiv:2607.18016

    Closing the Loop in Humanoid VLA: Persistent 3D Object Tokens for Verifiable Loco-Manipulation

    • It points out that robots fail on long tasks not because they cannot understand instructions, but because the object state used for action differs from the object state used for verification.
    • POT binds objects to role slots such as target, destination, support surface, and handoff partner, and refreshes the 3D position grounding after each action chunk.
    • Object records are turned into fixed-format tokens containing 33 features per slot across 8 slots and inserted into the input sequence of the whole-body action model.
    • The same record is used to verify grasp, placement, and release through geometry-based checks, and if it fails, the system triggers re-observation, retry, and replanning.
    • On eight task groups with a real Unitree G1, it improves the same-condition GR00T-N1.7 baseline from 39/80 to 71/80, and cup stacking jumps from 1/10 to 8/10.
    • In ablation, tokens alone improve from 15/40 to 31/40, verification alone gives 22/40, and using both gives 34/40, showing that token conditioning is the main driver of performance.
    AbstractPDF
  19. 677RoboticsarXiv:2607.18060

    RoboHarness: Memory-Driven Orchestration of Heterogeneous Robot Policies for Long-Horizon Planning

    • Instead of solving long tasks with a single policy, we reformulate the problem as coordinating heterogeneous policies with different capability boundaries.
    • Existing planning methods assume homogeneous skills with fixed, non-overlapping capabilities, but we point out that the capability boundaries of real policies change with context.
    • We use three groups of skills, understanding, memory, and self-evolution, to handle context interpretation, capability-boundary estimation, and experience-based correction during execution.
    • The memory bridge retrieves trajectories associated with the next policy, estimates that policy's normal input region, and guides the robot into that region to make handoff stable without retraining.
    • In ablation tests on 500 long-horizon tasks, the full system reaches 86.0% success, removing the memory bridge drops it to 60.4%, removing understanding skills drops it to 37.6%, and using only a single policy yields 0%.
    • In 135 real-robot trials, success drops from 86.7% to 66.7% under block re-obscuration perturbations, but remains 80.0% even after partial dismantling of the structure.
    AbstractPDF
  20. 676Machine LearningarXiv:2607.17247

    Distilled Reinforcement Learning for LLM Post-training

    • Reinforcement learning gives one reward per response, so it is hard to know which intermediate reasoning step to praise, and it mostly reinforces what the student can already do rather than adding new knowledge.
    • On-policy distillation forces the model to follow the teacher distribution through KL divergence, creating a dilemma in which there is little to learn if the teacher is too similar and too much noise if it is from a different family.
    • Distilled RL does not add a separate imitation objective; instead, it redistributes the learning signal of RL token by token using the reverse importance ratio between the teacher policy and the old student policy.
    • It consists of three parts: reverse importance sampling with ratio clipping, negative-sample reset that restores the weight of negative-advantage responses to 1, and response-level geometric normalization that sets the geometric mean of ratios within a response to 1.
    • Without negative-sample reset, mean pass@1 drops by 8.81 points on Qwen3-4B and 6.39 points on DSQW-1.5B, making it the most important of the three parts.
    • The gains are especially large when the teacher and student belong to different families. In that setting, on-policy distillation remains below RL, while Distilled RL beats all baselines.
    AbstractPDF
  21. 675Computer VisionarXiv:2607.18227

    FlowMimic: Mask-free Visual Editing and Generation with Pixel-pair Warped Flow Field for Online Video Editing Data Generation and Modality Mimicry

    • The core claim is that video editing training does not need truly natural video pairs, only pixel correspondences from the first frame that remain temporally consistent.
    • It converts image-editing samples into video-editing samples in real time by turning both the input image and the target image into 3D grids and building a 4D temporally warped flow field over pixel pairs through grid inverse sampling.
    • It treats an image as a one-frame video and uses two modality-mimic losses so that T2I mimics the realism of T2V and V2V editing mimics I2I editing, which converges faster.
    • It introduces sense-related tasks such as referring expression segmentation, together with edit-region-aware latent-level and attention-level losses, so the model can locate the edit region without a separate MLLM or mask input at inference time.
    • It continues training from Wan2.1-T2V-1.3B, and when the learning rate is warmed up from 5e-6 to 1e-5, video-editing behavior appears within a few thousand steps.
    • Without using any specially made video-editing data, it still handles a wide range of editing tasks and even shows SAM3-like ability to segment and track objects specified by language in video.
    AbstractPDF
  22. 674Computer VisionarXiv:2607.17967

    Fine-Detail Monocular Geometry Estimation with Self-Guided Sparse Volumetric Refinement

    • It diagnoses the limitations of existing monocular geometry estimation models as a structural mismatch rather than a capacity problem. When 3D information is decoded in a 2D coordinate system, features blend across depth boundaries.
    • The solution is to lift geometric modeling from the 2D image plane into 3D space, defining feature neighborhoods by 3D distance instead of screen distance.
    • It converts the point map into (X/Z, Y/Z, log Z) so that only log depth needs to be corrected, and quantizes this value to resolution D to build a sparse voxel shell.
    • A sparse 3D U-Net predicts log-depth residuals at each iteration, and the result redefines the voxel structure for the next iteration. Because of this self-guided scheme, computation is proportional to the number of occupied voxels.
    • In replacement experiments with a parameter-matched 2D U-Net, local metrics were similar to or worse than the base model, showing that the improvement comes from 3D inductive bias rather than capacity.
    • It was trained with K=3, but even when K was increased to 7 at inference time, the metrics held steady or improved slightly, indicating stable convergence of the learned correction.
    AbstractPDF
  23. 673Computer VisionarXiv:2607.17585

    Pixel-Space Diffusion Transformers

    • It points out that latent diffusion has an information bottleneck because the VAE's lossy compression irreversibly removes high-frequency texture and sharp boundaries.
    • It identifies the core problem as a mismatch between the objectives of a compressor trained for reconstruction and a diffusion model trained for generation, which makes end-to-end optimization difficult.
    • Moving to pixel space enables single-stage training, but it introduces new challenges in noise schedules, loss weighting, token efficiency, and scalable architecture design.
    • Recent methods are introduced as x0 prediction approaches that directly predict the clean original image and speed convergence by keeping the training path on the data surface.
    • It summarizes the trend toward splitting global structure and fine texture across different components through multi-branch designs, frequency separation, and finer tokenization.
    • It suggests that if images, text, and task conditions are placed in a shared token space and trained with a single transformer, the field can move toward unified vision foundation models that do both understanding and generation.
    AbstractPDF
  24. 672ResearcharXiv:2607.18238

    Color superconductors and holon metals from doping a Fractional Chern insulator

    • Using the parton construction c(r)=f1(r)f2(r)f3(r), the paper places the metallic and superconducting phases of a doped fractional Chern insulator within a unified framework.
    • Each color has three hole pockets across three valleys, so the charge minus e over 3 holons form nine pockets in total.
    • The SU(3) gauge field mediates pairing in the color-antisymmetric channel, allowing a gas of charge-e/3 anyons to enter a charge-2e superconducting state directly without an intermediate binding step.
    • If all nine pockets are weakly paired, c- becomes minus 3 over 2, which differs from the BCS expectation for an f-if superconductor, so an anyonic superconductor can break the usual relation between central charge and pair angular momentum.
    • Adding a particle-hole Higgs field yields two Z3 orthogonal metals with one or three pockets, and reducing valley symmetry to a triangular-lattice space group allows a three-pocket U(1)^2 holon metal that preserves C3.
    • In a chemical-potential-tuned FCI, a continuous transition to superconductivity is impossible in Z3 metals but possible in SU(3) or U(1)^2 holon metals, so the authors argue that the nine-fermion theory is a natural starting point.
    AbstractPDF
  25. 671Computer VisionarXiv:2607.18217

    HOMIE: Human-object Centric Video Personalization via Multimodal Intelligent Enchancement

    • The problem setting unifies inter-subject cases, where the reference images are of different targets, and intra-subject cases, where they are multiple views of the same target.
    • It points out the limits of two existing approaches: aligning multimodal model features to the text-embedding space weakens the control power of the text encoder, while replacing the text encoder entirely with a multimodal model introduces a large realignment cost.
    • GMG, the first proposal, adds global representations extracted from the multimodal model to the video tokens during query and key computation, which injects knowledge into self-attention.
    • MRE, the second proposal, uses learnable embeddings that distinguish token modality and reference index, which prevents errors such as duplicating the same object or text map as separate entities.
    • In performance, text recognition accuracy improves by 21.8 percent relative to SkyReels-V3, DINOrec reaches 0.696 and is best among the compared methods, and 40 human raters prefer the overall quality 64.7 percent of the time.
    • Ablation studies show that removing GMG drops face similarity from 0.786 to 0.697, and removing MRE drops text accuracy from 0.452 to 0.376.
    AbstractPDF
  26. 670LLMs / NLParXiv:2607.18213

    SWE-Pruner Pro: The Coder LLM Already Knows What to Prune

    • The core claim is that there is no need to recreate pruning signals outside the model, because the backbone already encodes that judgment in its representations when it reads tool outputs.
    • As evidence, the authors froze Qwen3-Coder-Next and ran a logistic-regression probe over line-averaged hidden states, reaching an AUC of 0.83 and a best F1 of 0.63 on held-out data.
    • The method uses the final-layer hidden states from the prefill stage, and a small head reads them to score tokens and aggregate them into line-level keep or delete decisions.
    • Two design choices improve performance: a length-aware embedding that includes the number of output lines, and a balanced focal loss that keeps the keep and delete ratios aligned per sample.
    • The result is 39% token savings on SWE-QA-Pro and 30% on Oolong across two open-weight backbones and four multi-turn benchmarks, and with MiMo-V2-Flash it also improves Oolong accuracy by 2.2 points and SWE-Bench Verified solve rate by 3.8 percentage points.
    • The extra cost is limited. In a replay setup with the head attached inside the inference engine, it adds only 15.0% wall-clock time over total generation time and requires no separate model call.
    AbstractPDF
  27. 669Computer VisionarXiv:2607.17423

    TimeLens2: Generalist Video Temporal Grounding with Multimodal LLMs

    • It defines video evidence finding as a problem over a variable-size set of temporal segments rather than a single interval, so it can handle long videos, repeated scenes, first-person video, and question-form requests in one model.
    • It solves data quality by stepwise verification instead of labeling everything at once, using candidate proposal, independent re-search, agent agreement, semantic verification, and boundary refinement.
    • Its key technique is the temporal Wasserstein reward, which treats the predicted and ground-truth intervals as uniform distributions and computes the exact 1D W1 distance to give dense feedback without explicit matching.
    • Overlap-based rewards such as tIoU give zero signal when the predicted interval does not overlap the ground truth at all, while Wasserstein reward distinguishes near misses from far misses in that case.
    • Empirically, the fraction of groups with all equal rewards and no learning signal drops from 13.8% to 3.6%, and 75.8% of the groups that were all zero regain a meaningful ranking.
    • The practical takeaway is that a much smaller model beats much larger ones, which is strong evidence that making answers temporally grounded and human-checkable works.
    AbstractPDF
  28. 668Machine LearningarXiv:2607.18110

    LLM-as-a-Coach: Experiential Learning for Non-Verifiable Tasks

    • A single score is an information bottleneck because it cannot distinguish answers that receive the same top score, especially at the upper end.
    • The solution is to use the same feedback model as a coach rather than a judge. The coach turns answer evaluation into generalized, reusable advice, or experiential knowledge.
    • That advice is given to the teacher model as context and internalized by the policy through on-policy context distillation, which minimizes token-level reverse KL divergence between the teacher and the policy output. At inference time, neither the coach nor the advice context is needed.
    • The motivation is illustrated by feedback bandwidth. A 1 to 10 integer score carries 3.3 bits per sample, while a 1024-token piece of advice over a 150,000-word vocabulary theoretically carries 17,600 bits.
    • Across two policy families, EL outperformed rubric-based reinforcement learning on held-out and novel benchmarks, whether the feedback model was the policy itself or a commercial API model.
    • While RL is prone to reward hacking because it learns to chase reward on the training set, EL is a distribution-matching method that follows the teacher distribution and therefore reduces that failure mode.
    AbstractPDF
  29. 667RoboticsarXiv:2607.18231

    FM-VLA: Force-based Memory for Vision-Language-Action Models in Contact-Rich Manipulation

    • It identifies a problem with contact-rich repetitive tasks: because the screen changes little, visual memory alone cannot track progress.
    • It first trains a variational autoencoder on force and torque time series with reconstruction as the objective, then freezes the encoder and uses the latent representation as memory tokens.
    • Using force memory alone makes pre-contact motion unstable, so it supplements it with a short record of joint states.
    • It achieves an average success rate of 83.3% over three tasks, outperforming pi-zero without memory at 27.8%, TA-VLA with a short force window at 22.2%, and visual-memory pi-MEM at 53.7%.
    • Inference latency increases by only 3.3 ms, from 60.7 ms to 64.0 ms, whereas visual memory grows from 100 ms to 190 ms depending on the number of frames.
    • Eight memory tokens were optimal, while increasing the count to 32 exceeded the token budget of the pretrained action module and hurt performance.
    AbstractPDF
  30. 666RoboticsarXiv:2607.18236

    Patch Policy: Efficient Embodied Control via Dense Visual Representations

    • The core claim is simple: dense visual features, not a giant generative model, are what visual motor policies really need.
    • The method is a minimal extension. It concatenates all patch tokens from a frozen ViT and uses a block causal mask that lets tokens within a frame see each other while blocking information from the future across frames.
    • The policy head can be swapped. It works with both VQ-BeT and Diffusion Policy, and the encoder is not fine-tuned.
    • The results show a 40% relative improvement over global features on four simulation tasks, Push-T, LIBERO Goal, BlockPush, and Cube, and on three real Franka robot tasks.
    • Efficiency is a major strength. The VQ-BeT-based system runs in 10.99 ms at inference even with DINOv2 patches, compared with 61.71 ms for OpenVLA-OFT, and training takes 6.5 GPU hours versus 16 GPU hours.
    • Reducing the number of patches hurts performance. On Push-T, 256 patches score 0.69, while 64 patches score 0.52 and 1 patch scores 0.48, which shows that spatial resolution is the key to precise manipulation.
    AbstractPDF
  31. 665RoboticsarXiv:2607.17977

    RynnBrain 1.1: Towards More Capable and Generalizable Embodied Foundation Model

    • The core claim is that it is not enough to understand vision well; the representation and output must be immediately usable for robot manipulation.
    • As a method, it adds contact-point prediction, meaning a grasp center and gripper-plane rotation angle, and native 3D localization as pretraining tasks, and 3D localization is included in the 2B and 9B models.
    • The architecture is a decoder-only vision-language model based on Qwen3.5, and it uses DeepStack and Interleaved MRoPE to handle images, multi-view images, and video over long contexts.
    • To unify multiple robots under one policy, it uses a shared action space aligned by body part and masks only the axes that each robot supports.
    • As a result, the average success rate over three real-robot tasks is 86.67 percent for RynnBrain-VLA, 73.33 percent for GR00T N1.7, 65.00 percent for π0.5, and 60.00 percent for the Qwen-based model.
    • The generalist version trained on multiple tasks and multiple robots performs better than task-specific training, with success rising from 86.67 percent to 91.67 percent, which supports the idea that heterogeneous robot data can complement one another.
    AbstractPDF
  32. 664Computer VisionarXiv:2607.17965

    Exploration Matters for Escaping the Blur Trap in 3D Gaussian Splatting

    • The core argument is that blur in 3DGS comes not from insufficient data or model capacity, but from bias in the optimization dynamics themselves.
    • Theoretically, it proves that 3D position updates derived from 2D gradients are orthogonal to the camera-to-Gaussian viewing ray, and reports that the 2D position component dominates the other two components by two to three orders of magnitude throughout training.
    • It splits blur into two kinds: far-side blur caused by missing depth signals and near-side blur caused by alpha blending suppressing the signal to increase density.
    • The remedy is simple: periodically seed candidate Gaussians at random positions, prune bad candidates by opacity pruning, and randomly split some Gaussians regardless of accumulated gradients.
    • Across five datasets, Mip-NeRF 360, Tanks and Temples, Deep Blending, OMMO, and DL3DV, and in the 4DGS extension, image quality improves, with PSNR on Tanks and Temples rising from 23.73 to 24.37 and DL3DV from 27.16 to 28.43.
    • Ablation studies show that forcing more splits by lowering the density threshold only inflates the number of points without materially improving quality, confirming that the gain comes from exploration itself rather than from simply increasing the count.
    AbstractPDF
  33. 663AI / GeneralarXiv:2607.15901

    DSWorld: A Data Science World Model for Efficient Autonomous Agents

    • It is the first to propose the concept of a data science world model that predicts state transitions in data science tasks.
    • It introduces cost-aware routing, where lightweight tasks are executed directly and heavier tasks are predicted by an LLM simulator.
    • It speeds up RL agent training by about 14 times and retrieval-based reasoning by 3 to 6 times.
    • On the transition-prediction task, it achieves accuracy that is 35.6 percent higher than the strongest LLM baseline.
    AbstractPDF
  35. 661Information RetrievalarXiv:2607.15591

    RecGPT-V3 Technical Report

    • The Memory Hub manages user behavior histories as memory units compressed by 94.5%, so the system does not have to reprocess the full history on every request.
    • It solves the tag-item information bottleneck with a hybrid multimodal model in which the LLM directly infers Semantic IDs.
    • It compresses 3,000-token reasoning into 15 tokens using latent CoT tokens, a 200x reduction, while keeping the content recoverable when needed.
    • In live A/B tests, it improved IPV by 1.28%, CTR by 1.00%, and GMV by 3.97%, while cutting GPU cost by 52.4%.
    AbstractPDF
  36. 660RoboticsarXiv:2607.15330

    Xiaomi-Robotics-1: Scaling Vision-Language-Action Models with over 100K Hours of Real-World Trajectories

    • An automatic VLM-based labeling pipeline is applied to more than 100,000 hours of real UMI manipulation trajectories.
    • The pretraining stage shows a clear scaling law, with performance improving consistently as both data and model size increase.
    • On RoboCasa365, it reaches 57.6 percent, which beats the previous best of 46.6 percent.
    • On a real robot, it learns new tasks from less than 10 hours of data and reaches an average success rate of 75 percent.
    AbstractPDF
  37. 659Computer VisionarXiv:2607.16107

    Audio-Visual Flamingo: Open Audio-Visual Intelligence for Long and Complex Videos

    • It releases the AV-Skills dataset, consisting of about 7 million caption and QA instances.
    • It designs a three-stage curriculum that progresses from short-video recognition to reasoning over long videos of up to 15 minutes.
    • TAVIT precisely maps intermediate reasoning steps to video and audio timestamps.
    • It achieves 60.2% on MMOU, the best performance among comparable open-source models.
    AbstractPDF
  38. 658LLMs / NLParXiv:2607.16051

    Loop the Loopies!

    • It introduces a layer-loop method and shows that it is more efficient for MoE than the existing model-loop approach.
    • It outperforms a vanilla 30B-A3B model under the same compute budget.
    • It records state-of-the-art math reasoning with 92.10% on AIME and 94.21% on AMC.
    • It achieves gold-medal-level performance at IMO 2025 and IPhO 2025 without tools.
    AbstractPDF
  39. 657Machine LearningarXiv:2607.16097

    Understanding Reasoning from Pretraining to Post-Training

    • It runs 36 pretraining-RL combinations with models from 5M to 1B parameters and discovers a scaling law in which pretraining loss predicts post-RL performance.
    • RL increases the probability of existing correct answers on easy problems and surfaces answers that were almost absent during SFT on hard problems.
    • The result shows that allocating a larger share of the total compute budget to RL becomes optimal as the overall budget grows.
    • The same pattern appears not only in chess but also in math, across a 1B language model and 10B to 200B tokens, where longer pretraining improves RL efficiency.
    AbstractPDF
  40. 656AI / GeneralarXiv:2607.13095

    Full-Pipeline Inference Optimization for MiMo-V2.5 Series: Pushing Hybrid SWA Efficiency to the Limit

    • It separates the KV cache for full attention and sliding window attention, compressing SWA storage to the theoretical limit of O(W), which is about 7x more efficient than before.
    • It builds GCache, an RDMA-based distributed cache infrastructure that shares KV caches efficiently across multiple nodes.
    • It redesigns the prefix cache tree to be SWA-aware and fixes accuracy bugs that the old prefix matching approach caused under SWA.
    • It also optimizes the multimodal pipeline end to end, including GPU image preprocessing, parallel video decoding from 156 seconds to 23 seconds for a 1-hour video, and cross-request batching for the encoder.
    • It improves MTP, or multi-token prediction, so that it also works in prefill and speeds up the first 128 decoded tokens by 2.3x.
    • It shows that resolving only the NUMA contention improves end-to-end performance by about 10%, which means system-level details matter a lot.
    AbstractPDF
  41. 655Machine LearningarXiv:2607.13188

    Concurrent Image Understanding and Generation: Self-Correcting Coupled Markov Jump Processes

    • It generates text and images simultaneously in a single decoding process, with each side verifying the other's decisions.
    • It proposes CO2Jump, a training-free sampler that outperforms prior methods on image editing and visual reasoning tasks.
    • It adds a remasking mechanism so that tokens once fixed can be hidden again when evidence changes, allowing the model to correct contradictions on its own.
    • It builds and plans to release three large multimodal datasets: JEdit-1M, JMaze-200K, and JNono-200K.
    • Performance improves steadily as the number of denoising steps increases, showing that the benefits of cross-modal coupling accumulate.
    AbstractPDF
  42. 654Machine LearningarXiv:2607.15207

    BadWAM: When World-Action Models Dream Right but Act Wrong

    • We are the first to uncover a new vulnerability in WAMs, showing that imagination and action can be separated.
    • An attack that targets action only reduces robotic task success from 96.5% to 43.1%, cutting it by more than half.
    • We design a stealth attack that preserves imagination while quietly corrupting only action, demonstrating that existing safety monitors can be bypassed.
    • The attack transfers from one WAM variant to another, confirming that the vulnerability is not specific to a single implementation.
    • Simple preprocessing defenses are not enough, which suggests a new safety approach that directly checks synchronization between action and imagination.
    AbstractPDF
  43. 653Machine LearningarXiv:2607.13491

    DeepLoop: Depth Scaling for Looped Transformers

    • The authors mathematically prove that the gradient accumulation in looped transformers with shared layers makes the usual DeepNorm scaling insufficient.
    • Simply increasing the exponent from one quarter to one half, with alpha equal to (2N) to the one-half power and beta equal to (8N) to the negative one-half power, stabilizes training as the number of loops grows.
    • On GPT-2 small and medium scales, increasing the number of loops consistently improves performance over prior methods.
    • When applied to the hierarchical reasoning model, or HRM, on the ARC-AGI reasoning benchmark, accuracy rises from 36.5 percent to 39.75 percent, which is a gain of 3.25 percentage points.
    • Because it changes only the scaling formula and adds no extra parameters, the method is essentially free to apply.
    AbstractPDF
  44. 652Computer VisionarXiv:2607.15278

    Hierarchical Denoising For Multi-Step Visual Reasoning

    • We solve the limitations of both fast but dumb streaming and smart but slow bidirectional video methods with a tree-structured approach.
    • Across six multi-step reasoning tasks, including maze solving, Sokoban, and Hanoi towers, success rises from 34.22 to 60.29, a 76.2% improvement.
    • The streaming version runs at 0.70 seconds per frame, 54x faster than bidirectional diffusion, while still achieving higher reasoning accuracy.
    • Using only 2% of the training data preserves 82.9% of the performance of the full data, showing strong data efficiency.
    • Real robotic arm maze experiments also show robust success, confirming applicability in the physical world.
    AbstractPDF
  45. 651Machine LearningarXiv:2607.15161

    On-Policy Delta Distillation

    • It argues that using the difference between a teacher's pretraining and post-training states, rather than the teacher's final output, transfers reasoning ability more accurately.
    • It consistently outperforms existing on-policy distillation methods across 14 benchmarks in math, science, and code.
    • It is validated broadly on recent models such as Qwen3, from 1.7B to 8B, and Gemma4, which makes the result credible.
    • The additional compute cost is only 8% to 28%, so the method can be added to existing distillation pipelines easily.
    • Its analysis shows that the delta signal concentrates on reasoning tokens such as hence and however, while suppressing exploratory tokens such as try and verify.
    AbstractPDF
  46. 650LLMs / NLParXiv:2607.15178

    T^2MLR: Transformer with Temporal Middle-Layer Recurrence

    • It shows that if only the middle layers, the transformer’s thinking layers, are connected recurrently, reasoning improves more than when the entire stack is repeated.
    • Recursing through only 20% of the full stack is enough, which maximizes reasoning capability while minimizing inference latency.
    • Even just adding the recurrent path and fine-tuning an already trained 1.7B model greatly improves mathematical reasoning accuracy, with GSM8K up 4.1 points and MATH500 up 5.2 points.
    • The inference overhead is only about 8%, so it can be used without much burden in production.
    • Training cost increases by roughly 2x to 4x, but it is a meaningful tradeoff given the gains in inference efficiency and performance.
    AbstractPDF
  47. 649AI / GeneralarXiv:2607.15257

    SearchOS-V1: Towards Robust Open-Domain Information-Seeking Agent Collaboration

    • It formalizes information retrieval as relational schema completion plus citation evidence, so progress can be measured at the entity and attribute level.
    • SOCM, with Frontier Task, Evidence Graph, Coverage Map, and Failure Memory, externalizes implicit progress into an explicit shared state.
    • Pipeline parallel scheduling assigns the next task immediately when an agent slot becomes free, which maximizes utilization.
    • The Search Tool Middleware Harness handles evidence extraction, budget enforcement, and loop detection outside the agent prompt.
    • It reaches 76.5 F1 on GISA, which is 13.4 points better than the strongest baseline, and 80.3 F1 on WideSearch, where it takes first place on every metric.
    AbstractPDF
  48. 648Machine LearningarXiv:2607.14530

    xHC: Expanded Hyper-Connections

    • It is the first method to extend Hyper-Connection beyond N=4 to N=16, because prior mHC methods suffered strong diminishing returns above N=4.
    • Temporal feature augmentation enriches write-back with nearby token information, and sparse residual updates at k=4 or N=16 balance cost and efficiency.
    • On an 18B MoE model, it delivers a +4.0 point gain over mHC while adding only 4.1% more FLOPs than vanilla.
    • The scaling law shows that vanilla requires 1.50x compute and mHC requires 1.19x compute to reach the same loss, using xHC as the reference.
    • xHC-Flash keeps sublayer memory traffic at 40C even with N=16, which is similar to mHC at N=4 with 34C.
    AbstractPDF
  49. 647Computer VisionarXiv:2607.14749

    WanSong v1.0 Technical Report

    • It abandons the autoregressive pipeline and uses pure diffusion to generate a five-minute song with vocals and background music as dual stems in a single pass.
    • Dual-stem tokens model vocals and accompaniment separately, so both can be generated at high quality without the usual quality drop from classifier-free guidance.
    • A 1024-compression VAE, which is half the compression of the previous 2048 setting, preserves fine acoustic detail and improves SI-SDR by 2.86 dB.
    • RLHF is used to optimize musicality, lyric accuracy, and prompt alignment, yielding a musicality score of 5.49 compared with 4.18 for Suno and 3.83 for Mureka.
    • Stable long-song generation is achieved through 6 million hours of multilingual data and three-stage curriculum training from 90 seconds to 300 seconds to SFT.
    AbstractPDF
  50. 646Computer VisionarXiv:2607.15273

    MeanFlowNFT: Bringing Forward-Process RL to Average-Velocity Generators

    • It is the first combination of forward-process RL with MeanFlow, which generates 1 to 4 steps by predicting interval-averaged velocity.
    • It applies the DiffusionNFT objective through an instantaneous velocity predictor derived from MeanFlow identity, while sampling still uses average velocity.
    • It theoretically inherits the policy improvement guarantee of DiffusionNFT.
    • On SD3.5-M, it beats SOTA on 6 of 8 metrics, and on Wan2.1 video it uses 4 steps to beat LongCat-Video RL's 50-step score of 82.57 with 84.33 VBench.
    • It shows consistent gains under test-time scaling as the number of steps increases.
    AbstractPDF