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収録論文 1,430資料更新日 Research Monitoringベンチマーク
  1. 732Computer VisionarXiv:2607.24591

    CameraAnything: Refilming Videos with Arbitrary Camera Control

    • It jointly controls extrinsic camera parameters such as position and orientation, and intrinsic parameters such as focal length and resolution.
    • The method combines per-pixel ray representations and resolution-aware 3D positional encodings with self-attention.
    • It handles original-resolution editing in a single generation pass without cropping or outpainting.
    • It creates paired training data with a synthesis pipeline based on synchronized multi-camera recordings.
    • Its real-world rotation error is 2.76 degrees, which is lower than 5.12 degrees for ReCamMaster and 13.99 degrees for TrajectoryCrafter.
    • It generalizes to real videos even when trained only on synthetic data.
    概要PDF
  2. 731Computer VisionarXiv:2607.24027

    Sol-Attn: Accelerating Video Generation Inference via On-the-Fly Attention Sparsification

    • It speeds up a pretrained model without training by sparsifying only the attention.
    • Block proxy scores are modeled as Gaussian, so blocks are selected using thresholds based on the mean and standard deviation.
    • It does not build a proxy-score map in memory, which reduces routing cost.
    • It reuses discarded block scores for approximate correction, so accuracy loss stays small even under aggressive sparsification.
    • It achieves end-to-end speedups of 2.1x for video generation and 2.3x for editing, without degrading quality.
    • When integrated with Sol-Engine, it reaches up to 5x acceleration for video generation.
    概要PDF
  3. 730Computer VisionarXiv:2607.24731

    Rethinking Classifier-Free Guidance in On-Policy Diffusion Distillation

    • OPD, which only matches CFG synthesis, fails to capture branch-level stagnation, so errors cancel out.
    • Privileged information in the teacher's negative branch causes the negative-branch asymmetry, or NBA, failure.
    • PDM separately constrains the positive prediction and the CFG direction, meaning positive minus negative, to prevent cancellation.
    • When the guidance scale changes, the baseline method breaks, but PDM remains stable.
    • By adjusting direction weights and the number of supervised states, it finds the best trade-off between accuracy and efficiency.
    • It improves control fidelity in pose, depth, and dense versus sparse scribble video control.
    概要PDF
  4. 729Computer VisionarXiv:2607.24241

    FilmBench: A Film-Grade Benchmark for Cinematic Video Generation

    • It constructed 1,169 prompts reverse-engineered by professional directors from scenes in award-winning films, of which 1,056 are multi-shot.
    • It evaluates cinematographic language along 3 axes, 12 elements, 35 detailed metrics for T2V, and 3 metrics specific to R2V.
    • The FilmOps automatic evaluator reproduced human rankings of 9 T2V and 7 R2V models with Spearman correlations of 0.95 and 0.96, respectively.
    • It enables comparison of dynamic aesthetics and multi-shot continuity, which existing web-video-centered evaluations miss, against filmmaking standards.
    • However, because the benchmark is tailored to film and professional video production, it does not directly represent performance on short vertical videos or general user-generated videos.
    概要PDF
  5. 728RoboticsarXiv:2607.24744

    Data Pyramid for Embodied Manipulation

    • It organizes embodied-manipulation data into five sources: real robots, UMI-based collection, first-person and third-person video, simulation, and general vision-language data.
    • It situates each source within the tension between scalability and robot alignment, comparing quality, diversity, reusability, and physical fidelity.
    • It analyzes how embodied brains, VLAs, and world-action models select and mix multiple data sources during pretraining.
    • This classification can serve as a reference framework for robotics researchers designing data mixtures around target capabilities and collection costs.
    • A limitation is that six challenges remain unresolved, including large-scale tactile data, failure and recovery records, and action alignment across different robots.
    概要PDF
  6. 727LLMs / NLParXiv:2607.24653

    Kimi K3: Open Frontier Intelligence

    • Kimi K3 is a mixture-of-experts model that activates 104 billion of its 2.8 trillion total parameters and supports visual input and a 1-million-token context.
    • It combines Kimi Delta Attention, Attention Residuals, and Stable LatentMoE, using 16 of 896 experts for each token.
    • Reinforcement learning across general reasoning, agentic work, coding, and multiple reasoning-effort levels strengthened long-horizon work and compositional generalization.
    • Its overall scaling efficiency is approximately 2.5 times higher than Kimi K2's, and all weights are released for use in frontier-level model research and deployment.
    • Although it was stronger than other open and commercial models in the evaluation suite, it still lagged behind top closed models such as Claude Fable 5 and GPT-5.6 Sol.
    概要PDF
  7. 726Computer VisionarXiv:2607.22148

    dRAE: Representation Autoencoder with Hyper-Spherical Codes

    • It clearly diagnoses the cause of VQ codebook collapse as a geometric mismatch between Euclidean distance and the geometry of high-dimensional representation spaces.
    • The best choice is a combination of angle-based, meaning cosine similarity, code assignment and Euclidean commitment loss, while using angles for everything hurts performance.
    • It keeps 100 percent codebook utilization even when the codebook scales to 131,072 entries, and it shows faster convergence and more stable training than VQ and IBQ.
    • On class-conditional ImageNet generation, it reaches gFID 4.45 with HSQ 65536, which is a large improvement over VQ, and it also performs well on text-to-image.
    • With only 12 million image-text pairs, it reaches GenEval 0.63 and DPG-Bench 80.58, which shows an efficient generation pipeline.
    概要PDF
  8. 725AI / GeneralarXiv:2607.22319

    Towards Trustworthy and Cost-Efficient Data Integration: From Naïve RAG to Agentic RAG

    • It diagnoses the three major problems in LLM-based data integration, namely hallucination, label dependence, and cost, as stemming from a lack of knowledge grounding.
    • It lays out an evolution path from Naive RAG to GraphRAG and KG-RAG and then to Agentic RAG.
    • In Agentic RAG, it proposes a structure in which five agents, Planner, Retriever, Reasoner, Decision, and Evaluator, collaborate.
    • It identifies major research challenges such as memory-retrieval knowledge conflict, batch retrieval noise, and agent initialization.
    • It emphasizes that a new benchmark is needed to evaluate reliability through evidence-based reasoning, cost efficiency, and autonomy at the same time.
    概要PDF
  9. 724RoboticsarXiv:2607.22530

    ViTacWorld: Scaling Visuo-Tactile World Models for Contact-Rich Robot Manipulation

    • It proposes the first action-conditioned visual and tactile world model for robots and builds a training pipeline that unifies simulation and real-world data.
    • By modeling touch as an additional view, it uses view-aware conditioning and cross-view attention to preserve visual-tactile consistency.
    • Adding generated rollouts to policy training improves the average success rate of the pi0.5 plus tactile policy from 42.5 percent to 67.5 percent, a gain of 25 points.
    • Pretraining and simulation data greatly improve generation quality across PSNR, SSIM, and LPIPS.
    • It can also be used as a policy evaluation tool to predict visual and tactile outcomes for a given action sequence.
    概要PDF
  10. 723RoboticsarXiv:2607.22535

    Robot-Factored World Models via Robot Rendering

    • It converts actions into nominal trajectories for a robot controller and renders them as input, passing action information without leaking future states.
    • By adding end-effector depth and scene depth, it enables contact and occlusion judgments that are impossible on the 2D image plane.
    • Across both SVD and Wan backbones, it improves PSNR, SSIM, and LPIPS over vector-conditioned baselines.
    • A new robot, xArm6 plus Inspire F1, not used in training can be applied zero-shot simply by changing the rendering.
    • Rendering human manipulation videos after retargeting the hand motions to the robot makes robot manipulation videos possible.
    概要PDF
  11. 722Computer VisionarXiv:2607.22531

    Twins: Learn to Predict Unified Representations with Focal Loss

    • It builds unified representations by simply concatenating ViT semantic features and VAE detail features channel by channel, with fixed token length.
    • It finds that when training a DiT, the ViT branch learns well but the VAE branch does not, and identifies three causes: frequency, dimensionality, and conditional dependence.
    • Applying focal loss to flow matching balances the VAE channels by weighting their errors, improving ImageNet gFID by up to 10.57 over MSE.
    • Compared with a single encoder such as SigLIP2, it preserves understanding performance while substantially improving reconstruction quality, as measured by PSNR.
    • With classifier-free guidance, it achieves an FID of 1.59, which is a solid generation quality result.
    概要PDF
  12. 721LLMs / NLParXiv:2607.22043

    Scaling Native Multimodal Pre-Training From Scratch

    • The scaling law for language objectives is almost unaffected by data composition, but multimodal objectives are very sensitive to the data ratio.
    • Text-heavy data is more efficient for larger models, so the optimal allocation of resources shifts toward model capacity.
    • It derives, in formula form, an efficiency frontier that specifies the optimal combination of model size, token count, and data ratio.
    • It confirms a cross-modal transfer effect in which native multimodal training also improves pure text-space reasoning ability.
    • It observes the natural emergence of multimodal in-context learning in a 3B-parameter model.
    概要PDF
  13. 720Computer VisionarXiv:2607.22534

    SM4RT: Learning Structured Motion Geometry for 4D Reconstruction

    • It proposes Structure-of-Motion, or SoM, a representation that expresses video motion as a combination of rigid motion bases rather than individual points.
    • It infers 3D geometry, motion, and scene motion structure in a single forward pass.
    • On the Kubric benchmark, it achieves state-of-the-art tracking accuracy, APD 90.62, and shows strong structure preservation on the deformation metric VM.
    • The DA3 backbone outperforms VGGT by a wide margin, and simple linear fusion is the most effective feature fusion method.
    • It is efficient at inference, taking 2.86 seconds per sample and using only 15.72 GB of GPU memory.
    概要PDF
  14. 719LLMs / NLParXiv:2607.22529

    Skill Self-Play: Pushing the Frontier of LLM Capability with Co-Evolving Skills

    • It uses reusable skills as the main carrier so that task diversity and verification reliability are both preserved.
    • A proposer, a solver, and a skill controller all evolve together in the reinforcement-learning loop.
    • The skill library creates about 20 new skills per iteration, updates existing skills, and drops useless ones.
    • With Qwen3-4B, it gains up to 42.9 points on tool use, BFCL, and 12.0 points on logical reasoning, ZebraLogic.
    • Dynamic skill evolution is the key, and it adds another 2.6 points in overall accuracy compared with self-learning without skills.
    • Even models that start out misaligned show a large turnaround effect.
    概要PDF
  15. 718Computer VisionarXiv:2607.21580

    GraphVid: Interactive Graph-Controllable Video Generation

    • It proposes the first framework for generating video conditioned on a scene graph, where objects are nodes and interactions are edges.
    • The Edge-Aware Graph Reasoning module performs message passing while reflecting the directional properties of edges.
    • It builds a new GraphVid-Bench dataset with about 27K interaction-centered videos.
    • It reduces FID by 39.9%, reduces FVD by 37.6%, and improves PSNR from 9.87 to 15.98 compared with Motion-I2V.
    • With only 0.6B trainable parameters, it is far more efficient than existing methods that use billions of parameters.
    • Because the graph representation is backbone-independent, it works with both LTX (2B) and Wan 2.2 (5B).
    概要PDF
  16. 717ResearcharXiv:2607.21367

    A solution to 2-copy distillability of Werner states

    • It completely resolves the 2-copy distillability problem for Werner states in arbitrary dimensions.
    • It proves that the 1-copy and 2-copy distillation thresholds are exactly the same, showing that the second copy provides no advantage.
    • It uses geometric estimates on symmetric and antisymmetric tensor subspaces as the key tool.
    • It resolves the first finite-copy step of the broader open problem of NPT-state distillability.
    • At the boundary case α = -1/2, the proof combines block-operator inequalities with variational methods.
    概要PDF
  17. 716LLMs / NLParXiv:2607.21051

    Sample-Efficient Learning from Agent Experience

    • The paper defines the problem of Experience Distillation, which distills an agent's interaction experience into weights, and proposes a solution.
    • It removes extra environment use by using a one-step branch rollout that resamples only the teacher's next action at each point in the collected trajectory.
    • It preserves 64.8 percent of the in-context learning effect, which greatly outperforms standard SFT at 3.8 percent recovery.
    • It matches reinforcement-learning baselines while using more than 9.6 times fewer environment samples.
    • The experimental details, including experience preprocessing, strengthened teacher inference, and branch packing, are validated empirically.
    概要PDF
  18. 715Machine LearningarXiv:2607.21427

    Context-weighted Discrete Flow Matching

    • It confirms experimentally that the more filled neighbors a token has, the lower its prediction entropy becomes.
    • It proposes a context-weighted sampler that reflects local context density in sampling without any extra training.
    • A Scaled Cross-Entropy loss reweights the training signal and lowers OpenWebText generation perplexity by 63%.
    • On molecule generation with QM9, it increases the number of valid molecules by 2.8x and the number of novel molecules by 1.9x.
    • It keeps quality similar to semi-autoregressive block-diffusion methods while preserving the flexibility of order-free generation.
    概要PDF
  19. 714AI / GeneralarXiv:2607.21503

    Agentic Context Management: Solving Agent Memory and Cost by Treating Them as Lifecycle and Architecture Problems

    • It redefines agent context management as a 5-stage lifecycle, design, extraction, scope, prediction, and compression, instead of treating it as simple storage and retrieval.
    • It proves mathematically that unlimited context accumulation increases token cost as O(n²), while naive summarization causes sharp drops in accuracy.
    • Its scope-aware design, which respects organizational layers such as user, customer, and client, simultaneously supports privacy isolation and organizational knowledge accumulation.
    • The reference implementation, Maximem Synap, achieves 92 percent on LongMemEval and 93.2 percent on LoCoMo.
    • It contrasts itself with commercial memory systems such as Mem0, Zep, and Cognee, which mostly focus on extraction and storage and do not cover the full 5-stage lifecycle.
    概要PDF
  20. 713ResearcharXiv:2607.21554

    Strategic Plan for Neutral Atom Quantum Computation

    • It presents a comprehensive roadmap covering all areas of neutral-atom quantum computing, including hardware, error correction, compilation, and networking.
    • It confirms scaling trends of about 1.8x more qubits per year and about 40% lower two-qubit gate error per year.
    • It emphasizes integrated photonics as the key technical path for scaling to large systems.
    • It explains how new error-correcting codes such as qLDPC can greatly reduce resource requirements compared with surface codes.
    • It summarizes the possibility and technical challenges of distributed quantum computing by networking multiple quantum processors.
    概要PDF
  21. 712Computer VisionarXiv:2607.21576

    Self-Supervised Learning of Structured Dynamics from Videos

    • The method proposes SDM, which structures video motion into two tokens, one for camera motion as the primary token and one for object motion as the residual token.
    • A lightweight model is placed on top of a frozen DINOv2 image backbone, which lets it learn dynamics representations without video pretraining.
    • It creates a new benchmark called ProbeMotion and measures performance systematically on synthetic and real videos.
    • With only weak supervision, it reaches performance similar to or better than VGGT trained on 17 datasets.
    • The primary token adapts automatically so that it captures the camera in camera-motion scenes and objects in static-camera scenes.
    概要PDF
  22. 711Machine LearningarXiv:2607.21585

    Expanding Flow Maps

    • It is the first to propose a new generative model structure called Expansion Flow, or EFlow, which increases dimensionality during generation.
    • It alternates expand and transport operations to generate high-quality samples in only one or two steps.
    • For molecular graph generation, it reduces the one-step FCD error to 0.44 versus 2.14 for the prior CFM method.
    • It also outperforms fixed-length models on language generation, which shows that variable-length generation does not hurt quality.
    • It demonstrates that the same framework works in both continuous domains, such as molecular structure, and discrete domains, such as graphs and language.
    概要PDF
  23. 710Computer VisionarXiv:2607.21072

    Show, Don't Tell: Evaluating Spatial Cognition in Generative Pixels Rather Than LLM Text

    • It proposes the ProVisE framework, which makes image generation models express spatial answers directly in pixel space.
    • It builds the SpatialGen-Bench diagnostic benchmark with 470 samples and 14 subtasks.
    • An agent builder automatically constructs and validates an evaluation protocol for a new benchmark.
    • Image generation models are competitive on intuitive visual-expression tasks.
    • Eighty-eight percent of failures come from errors in spatial reasoning itself, not from the visual communication channel.
    概要PDF
  24. 709RoboticsarXiv:2607.21582

    Scale Up Strategically: Learning Compositional Generalization via Bias-Aware Evaluation and Data Collection for Robotic Manipulation

    • It builds a framework that decomposes language instructions into five factors, color, verb, object, size, and space, to measure bias.
    • It finds a consistent bias structure across six base policies in the order color, object, spatial, verb, and size.
    • It proposes two quantitative metrics, FDR for pairwise bias across factors and FDH for the full ranking.
    • The bias diagnostics show that focusing data collection on weak factors improves generalization in real robots.
    • It achieves better performance than the previous full dataset even when using only half of the demonstrations.
    概要PDF
  25. 708ResearcharXiv:2607.21509

    Aspects of Closed Matricial Worlds

    • It analyzes the Hilbert space of two-dimensional quantum gravity with a positive cosmological constant using a matrix model.
    • It treats both the Wheeler-DeWitt equation and the path-integral perspective, and discusses their differences.
    • It explicitly computes the corrections that nontrivial topology contributes to the path-integral wavefunction.
    • It presents explicit results up to genus 6 and shows an analogy with topological gravity in the large-volume limit.
    • It names the double-scaling limit of the pure-gravity matrix model a 'closed matricial world'.
    概要PDF
  26. 707AI / GeneralarXiv:2607.21557

    OpenForgeRL: Train Harness-native Agents in Any Environment

    • It records harness model calls as proxies so standard RL code, such as veRL, can train on them.
    • Each rollout is isolated in a Kubernetes container, allowing complex environments to run safely.
    • Training and deployment share the same harness, eliminating train-deploy mismatch.
    • OpenForge-Claw reaches 55.9 pass@3 on ClawEval, and OpenForge-GUI reaches 37.7 on OSWorld.
    • RL improves self-verification and tool coverage, but error recovery remains weak.
    概要PDF
  27. 706Computer VisionarXiv:2607.21595

    3D-Aware VLMs with Implicit and Explicit Geometries

    • We inject both implicit geometry tokens (IGT) and explicit geometry tokens (EGT) into the VLM at the same time.
    • EGT encodes detailed 3D information, such as depth maps and point clouds, into lightweight embeddings.
    • A 3D-aware adapter effectively fuses 2D vision, implicit 3D, and explicit 3D information.
    • It uses only RGB video as input, so no additional 3D sensors are needed.
    • It achieves SOTA on four tasks: 3D video detection, 3D visual grounding, 3D dense captioning, and spatial reasoning.
    概要PDF
  28. 705Computer VisionarXiv:2607.21556

    Visual Contrastive Self-Distillation

    • It learns visual dependence by comparing the difference in token probabilities between the original image and an image with the content removed.
    • It needs no external teacher, privileged ground truth, visual evidence signal, or reasoning trajectory.
    • It adds no inference-time cost, because the EMA teacher is evaluated twice only during training.
    • On Qwen3-VL, it improves by +4.77 percentage points at 2B, +1.86 points at 4B, and +3.75 points at 8B.
    • It consistently outperforms previous self-distillation methods across 7 vision-language benchmarks.
    概要PDF
  29. 704RoboticsarXiv:2607.21588

    AXIS: A Growable Community-Driven Data Engine for Scalable Robot Manipulation

    • Browser-based MuJoCo-WASM teleoperation lets the system collect demonstration data without specialized equipment.
    • It builds a scalable community dataset with 207 tasks and more than 50K trajectories.
    • It integrates automatic success verification, quality filtering, trajectory smoothing, and IsaacSim augmentation into one pipeline.
    • Fine-tuning a pi0.5 model on AXIS data improves success rate by 5.8 percent and beats RoboCasa365 by 37.3 percent.
    • Performance improves consistently as the amount of data grows.
    概要PDF
  30. 703Computer VisionarXiv:2607.21594

    Streaming Multi-Agent Autoregressive Diffusion Model with World State Registers

    • It uses a world-state register, which is a learnable token, to explicitly manage shared state across multiple agents.
    • The register is grounded with three supervision signals: agent state, bird's-eye view, and scene text.
    • A Mixture-of-Transformers structure reduces interference between state updates and frame generation.
    • The Self-Forcing paradigm is used to update the register dynamically during long-horizon rollouts.
    • In two-agent Minecraft experiments, both logical consistency and generation quality improve.
    概要PDF
  31. 702LLMs / NLParXiv:2607.20911

    Tencent WorkBuddy Bench: A Multi-Domain Coding-Agent Benchmark with Contamination-Resistant Task Construction

    • It reverse-engineers real commit, pull request, and security scenarios to create tasks that cannot be found through web search.
    • The benchmark contains 260 tasks across four domains: 80 code, 70 web, 50 office, and 60 security tasks.
    • Each task is rewritten as a everyday, conversational persona request to prevent contamination.
    • Claude Opus 4.8 ranks first with an overall score of 75.0 percent, and GLM-5.2 ranks second with 72.9 percent.
    • The authors release the full code, environment images, and evaluation tools so anyone can reproduce and audit the benchmark.
    概要PDF
  32. 701Machine LearningarXiv:2607.21366

    Hilbert Operator for Progressive Encoding (HOPE): A Mathematical Framework for Deconstructing Learned Representations in Deep Networks

    • We model neurons as rank-1 operators in a Hilbert space, unifying pruning and merging under a single mathematical criterion.
    • It works without data or hyperparameters by using batch-normalization statistics.
    • Macro block eviction makes it possible to evaluate the entire residual connection path.
    • In DEFT fine-tuning, we propose merging redundant neurons to make room in the parameter space for new tasks.
    • The entire framework is mathematically rigorous, and concept-proving experiments validate it.
    概要PDF
  33. 700AI / GeneralarXiv:2607.21461

    AREX: Towards a Recursively Self-Improving Agent for Deep Research

    • It exploits the asymmetry between discovery and verification, where finding an answer is hard but checking it is easy, and uses verification as the starting point for the next research round.
    • A self-contained context compression tool keeps only the essentials from long conversations, making long-horizon research possible.
    • With a 4B model, it achieves 82.5 on BrowseComp and 52.4 on HLE, far outperforming similarly sized competing models.
    • It teaches search, tool use, and verification step by step through agent mid-training and long-horizon reinforcement learning.
    • It reduces the credit-assignment problem of sparse rewards by weighting the moments when key evidence is obtained.
    概要PDF
  34. 699Computer VisionarXiv:2607.21553

    SANA-Video 2.0: Hybrid Linear Attention with Attention Residuals for Efficient Video Generation

    • It mixes linear attention and softmax attention in a 3-to-1 ratio so that it can achieve both speed and quality.
    • The AttnRes technique improves information quality in deep layers by about 12 percent.
    • A single H100 GPU can generate a 720p video in 13 seconds, which is 120 times faster than Wan 2.2.
    • It directly learns the optimal balance point by showing that 25 percent softmax attention is the best trade-off between quality and efficiency.
    • Kernel optimization through Sol-Engine delivers an additional 3.58 times speedup.
    概要PDF
  35. 698LLMs / NLParXiv:2607.20062

    Solar Open 2 Technical Report

    • A 250B-A15B MoE structure scales up from Solar Open 1 (102B) while routing only 2.3% of the parameters.
    • Hybrid attention, 75% linear and 25% softmax, plus NoPE, supports a 1M-token context.
    • The Korean tokenizer is 24% more efficient than global models, meaning it represents the same Korean text with fewer tokens.
    • It ranks first among comparable open models and commercial APIs on average Korean benchmarks, and is nearly on par with DeepSeek-V4-Pro (1.6T) on Ko-GDPval.
    • It integrates 12 domain experts into a single model through multi-teacher on-policy distillation (MOPD).
    概要PDF
  36. 697ResearcharXiv:2607.19918

    Scalable Keyword Spotting via Modular Network Expansion

    • It freezes the entire base network, including batch-normalization statistics, so existing keyword performance is preserved at the pixel level.
    • With only 10K additional parameters, 6.7% of the 150K base, it improves FRR for new keywords from 6.46% to 4.37%.
    • It achieves better performance with fewer MACs, 16.34M versus 18.45M and 20.52M, than Adapter or LoRA.
    • A core-first decision rule preserves the exact behavior of the existing keyword threshold.
    • It can scale using only new keyword data, without access to the original training data.
    概要PDF
  37. 696Computer VisionarXiv:2607.20417

    ATSplat: Compact Feed-forward 3D Gaussian Splatting with Adaptive Token Expansion

    • An adaptive token expansion, or ATE, module automatically finds regions with large rendering error and adds Gaussians there.
    • Compared with dense baselines, it reduces the number of Gaussians by more than 5.7x while still reaching state-of-the-art quality.
    • It reconstructs 12 images at 512×960 in under 1 second and renders at 1,136 FPS.
    • Its 3D anchor-offset design decouples Gaussian placement from the input pixel grid.
    • Uncertainty predictions align well with the actual rendering errors, as shown visually.
    概要PDF
  38. 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.
    概要PDF
  39. 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.
    概要PDF
  40. 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.
    概要PDF
  41. 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.
    概要PDF
  42. 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.
    概要PDF
  43. 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.
    概要PDF
  44. 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.
    概要PDF
  45. 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.
    概要PDF
  46. 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.
    概要PDF
  47. 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.
    概要PDF
  48. 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.
    概要PDF
  49. 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.
    概要PDF
  50. 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.
    概要PDF