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Every archived paper with its summary, highest number first.

Papers 1,193Archive updated BenchmarksRSS feed
  1. 344AI / GeneralarXiv:2606.06453

    Vortex: Efficient and Programmable Sparse Attention Serving for AI Agents

    • A Python-embedded frontend allows rapid prototyping of sparse attention algorithms.
    • AI agents automatically generate and refine algorithms to achieve a 3.46x throughput improvement.
    • It accelerates GLM-4.7-Flash MLA by 4.7x and MiniMax-M2.7 229B by 1.37x.
    • It provides an efficient backend tightly integrated with modern LLM serving stacks.
    • It expands the applicability of sparse attention to emerging architectures and ultra-large models.
    AbstractPDF
  2. 343Machine LearningarXiv:2606.06458

    In-Context Multiple Instance Learning

    • A pretrained in-context learner trained on synthetic data solves MIL tasks from only a few labels.
    • It classifies in a single forward pass at inference time and does not require gradient updates.
    • It proposes and analyzes several synthetic data generators for bag-structured data.
    • Different generators capture complementary inductive biases.
    • A mixture-generator pretrained model outperforms supervised baselines on 12 MIL benchmarks.
    AbstractPDF
  3. 342Machine LearningarXiv:2606.06459

    Event Detection for Parameter-to-KPI Dependency Learning for AI-RAN

    • This work covers the first step of dependency learning for interference management among multiple AI control functions in AI-RAN.
    • We propose a pipeline that converts noisy continuous measurements into binary event indicators.
    • We introduce a new synthetic closed-loop traffic generator with embedded latent dependencies.
    • We evaluate the dependency recovery pipeline by formulating it as a significance detection problem.
    • We confirm that threshold calibration is a key determinant of event-detection quality.
    AbstractPDF
  4. 341SecurityarXiv:2606.06460

    Will the Agent Recuse Itself? Measuring LLM-Agent Compliance with In-Band Access-Deny Signals

    • It proposes a Recuse Signal that asks the agent to voluntarily withdraw through the protocol's existing channel.
    • It is designed as a cooperative governance mechanism such as robots.txt, not as a security boundary.
    • It was implemented with SSH banners and PostgreSQL NOTICE messages and tested in production environments.
    • Without the signal, agents completed tasks 100% of the time, and with the signal, they withdrew 100% of the time, showing a clean contrast.
    • A hierarchical response was observed in which only the most capable model resumed entry after operator approval framing.
    AbstractPDF
  5. 340AI / GeneralarXiv:2606.06462

    Benchmark Everything Everywhere All at Once

    • It autonomously handles the full benchmark-building pipeline, from query analysis to quality control.
    • It addresses the labor-intensive nature of existing benchmarks and the problem of rapid performance saturation.
    • It successfully generated 15 benchmarks covering text, multimodal, and domain reasoning.
    • Human evaluation and LLM-as-a-judge confirmed the high quality of the generated samples.
    • Continuous evaluation uncovered domain-specific reasoning weaknesses in current models.
    AbstractPDF
  6. 339LLMs / NLParXiv:2606.06467

    You Only Index Once: Cross-Layer Sparse Attention with Shared Routing

    • It proposes CLSA, which shares routing indices across layers in a KV-sharing architecture.
    • A single indexer performs top-k selection once and reuses the result across all layers.
    • It amortizes routing cost while preserving the accuracy of token-sparse attention.
    • It improves all major inference bottlenecks at once, including prefilling, KV cache, and decoding.
    • It achieves a 7.6x decoding speedup and a 17.1x overall throughput gain at 128K context length.
    AbstractPDF
  7. 338AI / GeneralarXiv:2606.06468

    Goedel-Architect: Streamlining Formal Theorem Proving with Blueprint Generation and Refinement

    • It uses a strategy that generates and refines blueprints, which are dependency graphs of definitions and lemmas.
    • Instead of recursive lemma decomposition, it avoids dead-end loops through parallel proofs and global refinement.
    • It uses DeepSeek-V4-Flash, a 284B-A13B open model, as the backbone.
    • It achieves 100 percent on MiniF2F-test, 88.8 percent on PutnamBench, 4 out of 6 on IMO 2025, and 11 out of 12 on Putnam 2025.
    • It reaches state of the art at up to 500 times lower cost than similar open-source pipelines.
    AbstractPDF
  8. 337StatisticsarXiv:2606.06469

    How abundant are good interpolators?

    • It establishes a large-deviation principle for the generalization error when a classifier is chosen at random from the set of interpolating classifiers.
    • It shows that almost all interpolating classifiers concentrate exponentially around the same generalization performance.
    • The analysis is carried out on two natural distributions: Gaussian mixture models and logistic models.
    • In the overparameterized regime, gradient descent and linear programming outperform random interpolators.
    • The result suggests that benign overfitting in practical algorithms is a nontrivial phenomenon rather than something obvious.
    AbstractPDF
  9. 336Machine LearningarXiv:2606.06470

    PC Layer: Polynomial Weight Preconditioning for Improving LLM Pre-Training

    • A low-order polynomial preconditioner stabilizes the singular value spectrum of the weight matrices.
    • The trained weights are merged back into the original architecture, so there is no inference overhead.
    • On Llama-1B, it improves performance over the standard transformer for both AdamW and Muon.
    • Theoretically, it guarantees geometric convergence of gradient descent when layerwise singular values are uniformly bounded.
    • It can be applied to existing LLM training pipelines without extra parameters or architectural changes.
    AbstractPDF
  10. 335AI / GeneralarXiv:2606.06473

    MLEvolve: A Self-Evolving Framework for Automated Machine Learning Algorithm Discovery

    • It uses Progressive MCGS to enable information flow across branches, solving the isolation problem of existing tree search.
    • Retrospective Memory jointly uses domain knowledge and task experience.
    • An adaptive coding mode that separates strategy planning from code generation improves stability in long iterative loops.
    • It achieves state-of-the-art mean medal rate and valid submission rate on MLE-Bench with a 12-hour budget, which is half the standard budget.
    • It outperforms AlphaEvolve on mathematical algorithm optimization and shows strong cross-domain generalization.
    AbstractPDF
  11. 334LLMs / NLParXiv:2606.06474

    Self-Augmenting Retrieval for Diffusion Language Models

    • It uses low-confidence tokens discarded during denoising as a lookahead signal for retrieval.
    • It is training-free, retriever-agnostic, and applicable to all discrete diffusion language models.
    • It exploits the fact that low-confidence tokens surface key entities before the output is complete.
    • It outperforms existing baselines on five multi-hop QA benchmarks.
    • It achieves up to 8x higher throughput than prior methods.
    AbstractPDF
  12. 333Machine LearningarXiv:2606.06475

    RREDCoT: Segment-Level Reward Redistribution for Reasoning Models

    • The method addresses the delayed-reward problem in chain-of-thought reasoning through segment-level reward redistribution.
    • We use the model itself to approximate optimal reward redistribution without additional generation.
    • This greatly reduces computational overhead compared with Monte Carlo sampling.
    • We perform credit assignment by identifying CoT segments that contribute to reaching the correct answer.
    • We systematically analyze design choices for CoT segmentation and state-value estimation.
    AbstractPDF
  13. 332Machine LearningarXiv:2606.06479

    Pretraining Recurrent Networks without Recurrence

    • It reduces RNN training to supervised learning over stepwise memory transitions and bypasses recurrent backpropagation.
    • It learns what to remember by generating memory labels with a Transformer-based encoder.
    • It captures long-range dependencies stably with an O(1) gradient path between every token pair.
    • It enables parallel training along the time axis, which has the potential to remove the bottleneck in scaling RNNs.
    • It outperforms standard BPTT on language modeling and pixel sequence modeling.
    AbstractPDF
  14. 331Game TheoryarXiv:2606.06480

    DNQ: Deep Nash Q-Network for Partially Observable n-Player Games

    • It trains bidding agents with a solver-in-the-loop equilibrium supervision framework.
    • A shared critic predicts a pairwise payoff matrix or an N-player payoff tensor to improve training efficiency.
    • The pairwise formulation greatly reduces compute cost compared with the exact N-player formulation.
    • The exact method becomes computationally infeasible as the number of agents increases.
    • It empirically shows the tradeoff between strategic fidelity and scalability.
    AbstractPDF
  15. 330LLMs / NLParXiv:2606.06481

    Operation-Guided Progressive Human-to-AI Text Transformation Benchmark for Multi-Granularity AI-Text Detection

    • The paper proposes OpAI-Bench, a benchmark that models the nine-stage progressive human-to-AI co-editing process.
    • It evaluates AI-text detectors at four granularities: document, sentence, token, and span.
    • It covers five representative AI editing operations across four domains.
    • It finds non-monotonic patterns in which intermediate versions of mixed-authorship text are harder to detect than either fully human text or high-ratio AI text.
    • AI detectability depends on the editing ratio, operation type, domain, and cumulative history.
    AbstractPDF
  16. 329Machine LearningarXiv:2606.06486

    Regret Minimization with Adaptive Opponents in Repeated Games

    • It introduces RP-Regret, a new metric for repeated games with adaptive opponents.
    • RP-Regret is fundamentally a nonconvex problem, and the paper proposes three algorithms to address it.
    • When all players minimize RP-Regret, they can learn a subgame-perfect equilibrium.
    • On the Stag-Hunt game, the methods converge to more cooperative and higher-utility solutions than existing approaches.
    • It identifies necessary conditions on the memory of comparison strategies and opponent strategies.
    AbstractPDF
  17. 328RoboticsarXiv:2606.06491

    TempoVLA: Learning Speed-Controllable Vision-Language-Action Policies

    • Existing VLA models learn only fixed speeds, but TempoVLA controls execution speed with an explicit speed condition.
    • VSTA augmentation preserves motion semantics while retiming demonstrations at arbitrary speeds.
    • It achieves bidirectional flexible speed control in both simulation and the real world.
    • Combined with a large multimodal model, it enables risk-aware dynamic speed control.
    • VSTA also improves the base 1x-speed performance by increasing data utilization.
    AbstractPDF
  18. 327Software EngineeringarXiv:2606.06492

    Code2LoRA: Hypernetwork-Generated Adapters for Code Language Models under Software Evolution

    • A hypernetwork generates repository-specific LoRA adapters to inject repository knowledge at inference time without token overhead.
    • It supports two modes, Code2LoRA-Static for static snapshots and Code2LoRA-Evo for evolving codebases.
    • Code2LoRA-Evo adapts to commit-level diffs with a GRU hidden state.
    • It introduces RepoPeftBench, a new benchmark built from 604 Python repositories.
    • It comes close to the repository-specific LoRA upper bound with 66.2 percent exact match on the static track and 60.3 percent exact match on the evolving track.
    AbstractPDF
  19. 326RoboticsarXiv:2606.06493

    HANDOFF: Humanoid Agentic Task-Space Whole-Body Control via Distilled Complementary Teachers

    • We propose a concise, modular interface between task planning and whole-body control.
    • We distill three experts, whole-body motion tracking, locomotion, and fall recovery, into a mixture of experts.
    • We achieve SOTA speed tracking and a wide manipulation workspace on Unitree G1 hardware.
    • A VLM-based agent planner enables control through natural-language commands.
    • It performs a wide range of tasks without task-specific data or controller fine-tuning.
    AbstractPDF
  20. 325Machine LearningarXiv:2606.06494

    TailLoR: Protecting Principal Components in Parameter-Efficient Continual Learning

    • It designs low-rank updates by using the singular-value basis, U and V, of the pretrained weights as a fixed reference frame.
    • A soft spectral penalty suppresses updates along principal-component directions to reduce interference between tasks.
    • Fine-grained adaptation is routed into the long-tail spectral coordinates that have higher flexibility.
    • It improves continual-learning performance for PEFT methods based on spectral decomposition.
    AbstractPDF
  21. 324LLMs / NLParXiv:2606.05122

    Self-Evaluation Is Already There: Eliciting Latent Judge Calibration in Base LLMs with Minimal Data

    • This paper finds that the capability is generally present even before any targeted training. With only a few-shot prompt, the base model already predicts multi-attribute quality scores from an external judge on open-ended responses across three benchmarks far above chance.
    • From just 160 unique examples, about 31 times fewer than the RL baseline, SEE improves held-out calibration across three benchmarks while preserving answer quality.
    • These results recast judge-aligned self-evaluation as a problem of extraction rather than acquisition.
    AbstractPDF
  22. 323Computer VisionarXiv:2606.05124

    Geometry Gaussians: Decoupling Appearance and Geometry in Gaussian Splatting

    • Since the success of 3D Gaussian Splatting (3DGS) for novel view synthesis, many works have explored how to use it for geometric surface representations as well.
    • This study shows that base 3DGS is inherently ill-suited to represent texture and geometry at the same time by training with complete ground-truth texture and geometry.
    • The authors also propose a simple solution that applies a single additional geometric opacity parameter to each splat and combines it with an optional transparency curation optimization pipeline.
    AbstractPDF
  23. 322Machine LearningarXiv:2606.05129

    Preserving Data Privacy in Learning Causal Structure with Fully Homomorphic Encryption

    • This paper proposes a fully homomorphic encryption (FHE)-based method that keeps data encrypted during transmission and computation by performing calculations on ciphertexts.
    • It also shows that the method can be portably extended to support differential privacy, making it easy to scale beyond FHE.
    • Experimental results show that the method achieves high consistency and comparable causal structures to the plaintext version on the tested datasets.
    AbstractPDF
  24. 321Machine LearningarXiv:2606.05130

    Towards Efficient and Evidence-grounded Mobility Prediction with LLM-Driven Agent

    • Supervised sequence models achieve high accuracy, but they require task-specific training and provide little transparency at the decision level.
    • This paper proposes a training-free LLM-driven agent framework that formulates next-location prediction as adaptive, evidence-controlled decision making.
    • Across three mobility datasets, AgentMob achieves the strongest overall performance among training-free LLM-based methods, and GPT-5.4 reaches 71.42 percent Acc@1 on BW, 33.14 percent on YJMob100K, and 33.50 percent on Shanghai ISP.
    AbstractPDF
  25. 320Machine LearningarXiv:2606.05131

    Deep Embedded Multiplicative DMD for Algebra-Preserving Koopman Learning

    • Deep Koopman methods learn flexible coordinates, while structure-preserving methods enforce operator identities over a fixed dictionary.
    • The result is a finite transition map over the learned latent cells.
    • These results suggest a practical rule for Koopman learning: learn the coordinates, constrain the algebra.
    AbstractPDF
  26. 319LLMs / NLParXiv:2606.05134

    Activation-Based Active Learning for In-Context Learning: Challenges and Insights

    • However, this paper reports a negative result: MLP outputs, viewed through the lens of large activations or the first four moments, do not correlate with example quality or task performance.
    • Specifically, across all tasks and models tested, the absolute Spearman correlation coefficient reached only 0.33 at most, showing that activation-based sampling should not be used for in-context learning.
    • The authors hypothesize that this may be due to superposition, the phenomenon where models represent more features than they have dimensions, which suggests methods such as Sparse Autoencoders (SAEs) may be a promising direction.
    AbstractPDF
  27. 318Machine LearningarXiv:2606.05138

    Generating Financial Time Series by Matching Random Convolutional Features

    • To mitigate this, recent approaches train generators to minimize the mismatch between non-learned feature representations of real and generated time series.
    • This paper shows that a generator trained by matching random SOCK features consistently outperforms signature-based and diffusion-based baselines across multiple small-sample financial datasets.
    • The authors further demonstrate SOCK's expressiveness on two-sample hypothesis testing and time-series classification tasks, where SOCK matches or surpasses existing unsupervised feature maps.
    AbstractPDF
  28. 317Machine LearningarXiv:2606.05139

    BBOmix: A Tabular Benchmark for Hyperparameter Optimization of Unsupervised Biological Representation Learning

    • Deep unsupervised learning architectures, especially autoencoders, are increasingly used in this field for dimensionality reduction and representation learning.
    • To make large-scale unsupervised HPO research broadly accessible, the paper introduces BBOmix, the first open-source tabular benchmark for unsupervised representation learning on real biological data.
    • The benchmark includes 105,000 evaluations across four AE architectures and seven multi-omics modalities in the TCGA and SCHC datasets.
    AbstractPDF
  29. 316Computer VisionarXiv:2606.05142

    GeM-NR: Geometry-Aware Multi-View Editing for Nonrigid Scene Changes

    • The paper demonstrates that the method can handle edits with large changes in geometry and appearance, which existing methods struggle with.
    • The authors perform extensive evaluations showing that the method improves consistency across a range of editing tasks, including generating a 3D representation of the edited scene.
    • Both quantitative and qualitative results indicate state-of-the-art performance in edit quality as well as geometric and photometric consistency across multiple viewpoints.
    AbstractPDF
  30. 315RoboticsarXiv:2606.05143

    HORIZON: Recoverability-Governed Curriculum for Physical-Domain Scaling

    • In on-policy learning, a new dynamic is useful only when it stays close enough to the current policy to generate corrective on-policy data instead of collapsing rollouts into irreversible failure.
    • Using quadruped locomotion as a physically challenging benchmark for embodied generalization, the paper introduces HORIZON, a checkpoint-based frontier curriculum that expands the physical domain only within the recoverability boundary of the current policy.
    • Taken together, the results frame physical-domain generalization as a continual-growth problem for embodied control and make recoverability the organizing principle for on-policy scaling.
    AbstractPDF
  31. 314Machine LearningarXiv:2606.05145

    Failed Reasoning Traces Tell You What Is Fixable (But Not by Reading Them)

    • The paper argues that this throws away an important signal, because some failures come from unlucky sampling and can be helped by more rollouts, while other failures are structural and resist resampling regardless of budget.
    • The authors propose that failed reasoning traces encode recoverability structure, meaning an inference-time signature of which test-time intervention can rescue a given failure.
    • Three task-level trajectory features derived from the structure of available interventions recover this structure from the distributional signature of failed rollouts rather than from the text of the rollouts themselves.
    AbstractPDF
  32. 313Computer VisionarXiv:2606.05149

    An Open-Source Two-Stage Computer Vision Pipeline for Fine-Grained Vehicle Classification using Vision Transformers

    • Evaluated on 3,805 annotated overtaking events collected from bike lanes in Ann Arbor, Michigan, the pipeline achieved 0.94 accuracy, and class-wise F1 scores ranged from 0.91 for minivans to 0.97 for SUVs.
    • Three of the four well-represented categories retained F1 scores above 0.90 even under domain shift.
    • The complete pipeline, including the inference script, training code, evaluation utilities, and model weights, is released as open-source software to support reproducibility and reuse across roadside video archives and bicycle safety research.
    AbstractPDF
  33. 312Neural NetworksarXiv:2606.05150

    Multi-Column RBF Neural Network Using Adaptive and Non-Adaptive Particle Swarm Optimization

    • Error correction, the state-of-the-art gradient-based learning method, chooses the optimal hidden units to improve accuracy.
    • Both ErrCor and PSO demonstrate improved results and competitive convergence.
    • Inspired by the success of MCRN, the authors propose two new approaches for improving PSO performance: multi-column RBFN with PSO, or MC-PSO, and multi-column RBFN with APSO, or MC-APSO.
    AbstractPDF
  34. 311Machine LearningarXiv:2606.05152

    Reinforcement Learning from Rich Feedback with Distributional DAgger

    • The paper shows that RL methods using existing self-distillation objectives based on reverse KL or Jensen-Shannon do not guarantee monotonic policy improvement, meaning the update can increase the probability of worse actions even when the expert has higher reward.
    • In contrast, it shows that forward cross-entropy allows monotonic policy improvement and provides regret guarantees.
    • It also shows that this objective improves Pass@N by optimizing a lower bound on the teacher's weighted success likelihood.
    AbstractPDF
  35. 310LLMs / NLParXiv:2606.05158

    Streaming Communication in Multi-Agent Reasoning

    • The paper introduces StreamMA, a multi-agent reasoning system that reduces latency by streaming each reasoning step to downstream agents as soon as it is generated, thereby pipelining neighboring agents.
    • Across eight reasoning benchmarks spanning math, science, and code, two state-of-the-art LLMs, Claude Opus 4.6 and GPT-5.4, and three topologies, Chain, Tree, and Graph, StreamMA outperforms both baselines.
    • Beyond these contributions, the authors discover a 'step-level scaling law': increasing the number of steps per agent consistently improves both effectiveness and efficiency, and this is a new scaling dimension that is orthogonal to and composable with agent-count scaling.
    AbstractPDF
  36. 309RoboticsarXiv:2606.05159

    X4Val: Learning Neural Surrogates for Variance-Reduced Policy Evaluation

    • It motivates an evaluation method that uses heterogeneous data sources, including simulation, historical policy logs, and data collected from related platforms or environments.
    • The paper provides both theoretical analysis and empirical evaluation for autonomous driving and real-world robot manipulation tasks, where X4Val achieves up to a 38.4% reduction in variance and shows consistent gains over strong baselines in these domains.
    • These results show that using unpaired heterogeneous data can substantially improve the sample efficiency of rigorous robot system verification.
    AbstractPDF
  37. 308RoboticsarXiv:2606.05160

    GRAIL: Generating Humanoid Loco-Manipulation from 3D Assets and Video Priors

    • This privileged setting better conditions 4D reconstruction, allowing model-based object tracking, human motion estimation, and interaction-recognition optimization to reconstruct metric 4D human-object interaction (HOI) trajectories while reducing depth ambiguity and shape mismatch.
    • The authors retarget the reconstructed motions to a humanoid robot and learn complementary general-purpose task trackers: an object-aware latent adapter for manipulation and a scene-aware tracker for terrain traversal.
    • Using only data generated by GRAIL, they learn egocentric visual policies through a sim-to-real pipeline and deploy them on the Unitree G1 humanoid, achieving real-world success rates of 84% on diverse object picking tasks and 90% on stair climbing.
    AbstractPDF
  38. 307LLMs / NLParXiv:2606.05161

    Beyond Text Following: Repairable Arbitration Reversals in Audio-Language Models

    • The paper examines this question using an audio-fixed counterfactual method that removes only conflicting text and measures the resulting change in model preferences.
    • Across five ALMs and four conflict tasks, sign flips appeared in 64.1 percent of conflict samples. In other words, the same audio-only branch preferred the audio-supported answer, while the combined branch preferred the text-supported answer.
    • Under a strict 5 percentage point fidelity-drop budget, GACL improves nAUC by 17.8 points over the best contrastive-learning baseline and transfers to vision-text mediation without retraining, with up to a 40.5 percentage point gain.
    AbstractPDF
  39. 306Computer VisionarXiv:2606.05162

    Controllable Dynamic 3D Shape Generation via 3D Trajectories and Text

    • To address this, the paper adopts 3D trajectories as controllable spatial guides that specify the exact path selected points should follow.
    • To robustly handle trajectory inputs with arbitrary configurations, from dense to sparse and unevenly distributed, we further propose shape-aware trajectory embeddings that map the input trajectory set into a shape-aware token set covering the entire object.
    • Quantitative and qualitative evaluations, along with user studies, show that this approach follows prompts more faithfully while preserving motion quality and generating more expressive motions.
    AbstractPDF
  40. 305Machine LearningarXiv:2606.05165

    STRIDE: Training Data Attribution via Sparse Recovery from Subset Perturbations

    • The paper proposes a shift in perspective: instead of estimating parameter changes, it models the functional effect of training data in activation space.
    • The authors introduce STRIDE, which stands for Steering-based Training Data Influence Decomposition, a framework that formulates TDA as a sparse recovery problem in the spirit of compressed sensing.
    • STRIDE achieves SOTA on LLM pretraining attribution while being one order of magnitude, specifically 13x, faster than existing methods.
    AbstractPDF
  41. 304LLMs / NLParXiv:2606.02545

    Transferable Self-Harm Surveillance from Emergency Department Triage Notes Using an Evidence-Augmented Machine Learning Approach

    • The approach achieved AUPRC scores of 0.887 ± 0.016 and 0.884 ± 0.012 on internal and external validation, respectively.
    • Prospectively, it achieved an AUPRC of 0.881 ± 0.008 at the development institution and 0.879 ± 0.012 and 0.816 ± 0.015 at two external institutions without site-specific retraining.
    • Its key advantage is that it can identify major self-harm methods with 95 percent accuracy, supporting more granular surveillance beyond binary classification.
    AbstractPDF
  42. 303LLMs / NLParXiv:2606.02548

    SN-WER: Script-Normalized WER for Multi-Script Indic ASR Evaluation

    • Before computing WER, the paper proposes Script-Normalized WER (SN-WER), a training-free, evaluation-only metric that transliterates both reference and hypothesis text into the standard script for each language.
    • In controlled stress tests, artificially induced WER inflation from romanization was reduced by 67%, and in lexical substitution control experiments sensitivity to semantic errors remained almost unchanged, with Delta SN-WER to Delta WER at about 1.09.
    • SN-WER is robust to transliteration choices and normalization changes, and it showed a low token collision rate of under 0.1% in the Indian language settings evaluated.
    AbstractPDF
  43. 302RoboticsarXiv:2606.02551

    AFUN: Towards an Affordance Foundation Model for Functionality Understanding

    • Existing methods usually cover only part of this task; they either localize task-relevant regions without specifying executable actions, or they predict actions but remain limited in scalability.
    • This paper presents ourmodel, a step toward an affordance foundation model for functionality understanding.
    • From a single RGB-D observation and a natural-language task description, ourmodel predicts a task-conditioned functionality mask that indicates where to interact and a 3D post-contact action curve that indicates how to interact.
    AbstractPDF
  44. 301Computer VisionarXiv:2606.02552

    Modeling Depth Ambiguity: A Mixture-Density Representation for Flying-Point-Free Depth Estimation

    • The authors trace this artifact to a standard modeling choice, namely assigning a single depth hypothesis to each pixel.
    • They address this with MDA, a mixture-density representation that allows multiple depth hypotheses and their associated probabilities to be predicted for each pixel.
    • Across different backbones, MDA substantially improves boundary reconstruction and removes most flying-point artifacts even under severe input blur, while adding only negligible runtime overhead.
    AbstractPDF
  45. 300Computer VisionarXiv:2606.02553

    LongLive-RAG: A General Retrieval-Augmented Framework for Long Video Generation

    • To make retrieval more discriminative, the authors introduce Window Temporal Delta Loss, which suppresses redundant local similarity and encourages embeddings to capture meaningful temporal change.
    • Experiments across several AR backbones and generation lengths show improved long-form video quality and the best average VBench-Long ranking.
    • As far as the authors know, LongLive-RAG is the first open-ended AR long-video generation method to formalize its self-generated latent history as content-addressable retrieval memory.
    AbstractPDF
  46. 299LLMs / NLParXiv:2606.02556

    HERO'S JOURNEY: Testing Complex Rule Induction with Text Games

    • The paper introduces HERO'S JOURNEY, a benchmark for rule induction in goal-directed episodic tasks, where agents must infer hidden rules from demonstrations and act accordingly through multi-step execution.
    • Evaluation of state-of-the-art LLMs found that the models show evidence of rule induction, but the ability is limited and uneven across tasks.
    • Induction-specific steering methods improve performance on attribute tasks but do not reliably improve procedural tasks, suggesting that the gap in procedural induction remains an open problem.
    AbstractPDF
  47. 298LLMs / NLParXiv:2606.02559

    From Layers to Submodules: Rethinking Granularity in Replacement-Based LLM Compression

    • Existing replacement-based methods share two design constraints: layer-level granularity and continuous selection.
    • Building on this intuition, the authors introduce SubFit, or submodule-level fitted residual replacement, which compresses LLMs at the submodule level. Attention and feed-forward submodules are selected non-contiguously, and each receives its own lightweight fitted residual bypass path.
    • SubFit operates after training and requires only calibration data.
    AbstractPDF
  48. 297RoboticsarXiv:2606.02562

    Permissive Safety Through Trusted Inference: Verifiable Belief-Space Neural Safety Filters for Assured Interactive Robotics

    • The paper proposes an algorithmic approach that explicitly considers the reliability of a robot's runtime reasoning module and uses conformal prediction to certify high-probability safety for BeliefSF.
    • The method leverages the structure of belief-space safety filtering by concentrating verification on the regions where reasoning is expected to be trustworthy.
    • On a simulated human-vehicle interaction benchmark, the approach validates much more permissive belief-space safety filters than standard conformal prediction baselines.
    AbstractPDF
  49. 296Machine LearningarXiv:2606.02563

    IntraShuffler: A Privacy Preserving Framework for Heterogeneous DP Federated Learning

    • Heterogeneous differential privacy (HDP) in federated learning allows clients to choose individual privacy budgets and supports budget-aware aggregation.
    • Experiments across four different datasets show that IntraShuffler reduces gradient reconstruction risk by more than 60% and lowers surrogate inference accuracy from 0.78 to 0.33, while maintaining similar model utility across multiple FL aggregation rules.
    AbstractPDF
  50. 295Computer VisionarXiv:2606.02564

    VLMs are Good Teachers for Video Reasoning via Adaptive Test-Time Optimization

    • State-of-the-art VGM delivers strong visual quality, but it often struggles to understand and follow task-specific rules, which leads to logical failures across diverse reasoning scenarios.
    • VLMs are difficult as solvers, but they have strong perceptual abilities that allow them to evaluate whether process constraints are satisfied and whether the final goal is achieved.
    • These results show that integrating VLMs as test-time teachers offers a promising paradigm for achieving generalizable video reasoning.
    AbstractPDF