Search: “inference”

The Imperfective Paradox Is Not Necessarily in Large Language Models: A Benchmark Failure Before a Model Failure

model-releasesarxiv-cs-cl

arXiv:2608.25005v1 Announce Type: new Abstract: The imperfective paradox provides a useful test of compositional semantic analysis. Recent work constructs an NLI benchmark and reports that models freq

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The Von-Neumann State-Space Transformer for neural decoding

DGX agent

arXiv:2608.25088v1 Announce Type: new Abstract: Cortical computation is strikingly low-dimensional: a handful of latent variables, carried in a neural population's activity, steer the higher-dimension

model-releasesarxiv-cs-lg

27 Aug 2026

Token-Oriented Semantic Communication with Pretrained Vision Transformers

arXiv:2608.25410v1 Announce Type: cross Abstract: Token communications realize the semantic communication principle at the granularity of transformer tokens, providing a promising direction for client

safetyarxiv-cs-cv

Tool Verification for Test-Time Reinforcement Learning

arXiv:2603.02203v2 Announce Type: replace-cross Abstract: Test-time reinforcement learning (TTRL) has emerged as a promising paradigm for Recursive Self-Improving AI (RSI) by adapting Large Reasoning

researcharxiv-cs-cl

V-Rubrics: Visual Faithfulness via Rubric-Based Reinforcement Learning

model-releasesarxiv-cs-cv

arXiv:2608.25580v1 Announce Type: new Abstract: Vision-language models can produce fluent answers that are insufficiently grounded in the visual evidence: a single unsupported object, chart value, or

Where to Look Matters: On-Policy Self-Distillation for Long-Video Understanding

arXiv:2608.25356v1 Announce Type: new Abstract: Vision-language models (VLMs) have made substantial progress in long-video understanding, with standard backbone models typically answering questions fr

safetyarxiv-cs-cv

A Heterogeneous Mixture of Experts Framework for Interpretable Machine Learning

model-releasesarxiv-cs-lg

arXiv:2608.24195v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) models provide a flexible framework for partitioning complex prediction problems into simpler local learning tasks through an

ALPHABET: A Laplace-Pole History Aggregator with Banked Exponential Transport

arXiv:2608.24051v1 Announce Type: new Abstract: Can a sequence model remain competitive with only a few thousand parameters and an explicitly auditable prediction interface? We introduce ALPHABET, a c

local-aiarxiv-cs-lg

Applications

Beyond Accuracy: A Dual-Judge Evaluation Protocol for Vision-Language Models in Legally Grounded Tasks

arXiv:2608.24258v1 Announce Type: new Abstract: AI systems are increasingly evaluated for legally accountable settings, where correct outputs must also be justifiable against an applicable legal stand

applicationsarxiv-cs-ai

CAFE: Self-Improving Search Agents Need Co-Evolving Feedback

arXiv:2608.24794v1 Announce Type: new Abstract: Outcome-supervised search agents learn when and how to retrieve evidence, but terminal rewards neither localize intermediate errors nor redirect an ongo

Confidently Wrong, Silently So: Auditing Undetectable Failures of a Deployed On-Device Language Model

arXiv:2608.23663v1 Announce Type: cross Abstract: Aligning deployed language models requires knowing when their outputs can be trusted, yet on-device models now ship to hundreds of millions of devices

Constraint-Guided Enterprise Data Mapping with Large Language Models

arXiv:2608.24218v1 Announce Type: new Abstract: Enterprise entity alignment must handle semi-structured records, implicit attributes, and unit or granularity mismatches. Manual matching is still commo

Diverse by Reasoning: Harnessing the Wisdom of LLM Crowds for Future Prediction

arXiv:2608.24001v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for future prediction, motivating the use of multiple models as a wisdom-of-the-crowd mechanism. Howe

DoublesEval: Diagnosing Multi-Agent Tactical Reasoning in Vision-Language Models via Professional Doubles Badminton

model-releasesarxiv-cs-cv

arXiv:2608.24439v1 Announce Type: new Abstract: Visual Language Models (VLMs) excel at describing visible scene content but struggle to reason about dynamic multi-agent interactions, where action sema

Eating for a Sustainable Planet: Personalized Sustainable Diet Recommendation via Constraint-Aware Decision-Making Modeling

arXiv:2608.24274v1 Announce Type: new Abstract: A sustainable diet represents a multi-dimensional synergy among four essential pillars: nutrition adequacy, economic affordability, cultural acceptabili

safetyarxiv-cs-ai

Elastic KV Cache for LLM Serving:A Working Reclamation Mechanism, and Why Chunked Prefill Already Closes the Gap

arXiv:2608.23658v1 Announce Type: cross Abstract: An LLM serving engine sizes its key-value (KV) cache once, at startup, permanently setting aside a reserve for the worst-case prefill activation. Duri

hardwarearxiv-cs-ai

Eluna: An Agentic LLM System for Automating Warehouse Operations with Reasoning and Task Execution

arXiv:2607.08960v2 Announce Type: replace-cross Abstract: Warehouse operations are governed by Standard Operating Procedures (SOPs) that encode complex, multi-system decision logic, which must be exec

Evaluating Multiple LLM Generations with Validated Task Coverage

arXiv:2608.24228v1 Announce Type: new Abstract: Many LLM applications are most useful when they provide several candidate outputs for comparison, validation, or combination. Predominant evaluation set

Agents

Evidence Blindness in Direct Corpus Interaction: Persistent Navigation with AtlasNav

arXiv:2608.24764v1 Announce Type: new Abstract: Large language model agents are moving beyond conventional retrieval-augmented generation toward direct interaction with external corpora. Direct Corpus

agentsarxiv-cs-ai

GATNextHop: A GAT for Shortest Path Routing with Cross-Topology Generalization

model-releasesarxiv-cs-lg

arXiv:2608.23917v1 Announce Type: new Abstract: Common shortest-path algorithms, such as Dijkstra's (SPF), that OSPF uses, provide exact routing solutions but must be recomputed for each network topol

GaussianWAM: Distilling Geometry and Semantics from 3D Gaussian Fields into World-Action Models

arXiv:2608.24714v1 Announce Type: new Abstract: World-Action Models (WAMs) jointly learn future visual prediction and action generation, using video dynamics as a representation-learning signal for ro

local-aiarxiv-cs-ro

Gen2Physics: Grounding Generated 3D Meshes in Physics via Multi-View Material Decomposition

arXiv:2608.23869v1 Announce Type: new Abstract: While state-of-the-art generative models produce high-fidelity 3D meshes, these outputs lack the physical properties required for interactive simulation

researcharxiv-cs-cv

Giraffe: A Mapping Architecture from Hidden Text Representations to Visual Embeddings for Efficient Graphic Design

arXiv:2608.23970v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have made significant progress in understanding and interpreting mul- timedia content. However, their ability t

Graph-Supervised Hierarchical Clinical Alignment for Radiology Report Generation with Large Language Models

arXiv:2608.24121v1 Announce Type: new Abstract: Radiology report generation (RRG) has recently benefited from large language models, which substantially improve report fluency. However, clinically fai

safetyarxiv-cs-cv

HMGCLIP: Heterogeneous Multi-Granularity Contrastive Learning for E-commerce Representation Learning

arXiv:2608.24467v1 Announce Type: new Abstract: Although recent Multimodal Large Language Models (MLLMs) have advanced general product understanding, they implicitly encode product information into gl

Hybrid Semantic Tool Discovery for Enterprise MCP Gateway: Architecture and Implementation

arXiv:2608.23992v1 Announce Type: cross Abstract: Large language model (LLM) agents invoke external tools to retrieve and reason over information beyond pretrained knowledge. The Model Context Protoco

Identifying Latent Declarative Representations of Code for Assisting Repository Migration

arXiv:2608.23619v1 Announce Type: cross Abstract: Legacy software repositories embed decades of domain knowledge in undocumented code, making understanding and modernization difficult. We treat a prog

It depends: Incorporating correlations for joint aleatoric and epistemic uncertainties of high-dimensional output spaces

arXiv:2608.24518v1 Announce Type: cross Abstract: Uncertainty Quantification (UQ) plays a vital role in enhancing the reliability of deep learning model predictions, especially in scenarios with high-

researcharxiv-cs-cv

Learning to Grade Efficiently: A Bandit-Driven Prompt-Selection Framework for Low-Cost LLM Essay Scoring

arXiv:2608.23814v1 Announce Type: cross Abstract: Large Language Models (LLMs) demonstrate strong capabilities in automated essay scoring (AES), but contemporary approaches typically employ fixed prom

Low-Rank Velocity Fields as a Structural Prior for Unsupervised 4D Medical Image Interpolation

arXiv:2608.24025v1 Announce Type: new Abstract: Endpoint-only unsupervised 4D medical image interpolation synthesizes intermediate volumes from sparsely sampled sequences with only the start and end v

local-aiarxiv-cs-cv

Applications

Markerless Pose Estimation for Resistance Training Technique Assessment

arXiv:2608.24384v1 Announce Type: cross Abstract: Resistance training can be a high risk activity, and safe form is essential to avoiding injury. Laboratory-based movement analysis provides quantitive

applicationsarxiv-cs-ai

Mechanistic Circuit Identification for Controllable Data Generation

arXiv:2608.24065v1 Announce Type: cross Abstract: While recent advances in data synthesis aim to curate high-quality datasets, most generation pipelines still rely on heuristic prompt-based control. T

safetyarxiv-cs-ai

MetaRAG: Belief-Action Aligned Policy Optimization for Agentic RAG

arXiv:2608.24214v1 Announce Type: new Abstract: Agentic retrieval-augmented generation (RAG) requires language models to decide when to continue searching and when to answer. Existing RL-based methods

safetyarxiv-cs-ai

Tutorials

Mixture of Channel Experts: Static Sparse Supports with Input-Adaptive Mixing for Pointwise Projections

arXiv:2608.23794v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) scales language models by routing each input through a small set of independently parameterized experts. We show that copying

tutorialsarxiv-cs-cv

Neurosymbolic Alignment for Physiologically-Safe Clinical Language Models

arXiv:2608.24534v1 Announce Type: new Abstract: Clinical LLMs can generate recommendations that are factually plausible yet physiologically unsafe. We investigate whether safety alignment can be impro

Not All Tokens Are Equal: Region-Aware Consistency Repair of Backdoors in MLLMs

arXiv:2608.24354v1 Announce Type: cross Abstract: MLLMs are increasingly deployed in user-facing applications, yet they inherit backdoor risks from the pipelines used to construct them: triggers may r

local-aiarxiv-cs-ai

Panning for Gold: Expanding Domain-Specific Knowledge Graphs with General Knowledge

arXiv:2601.10485v5 Announce Type: replace Abstract: Domain-specific knowledge graphs (DKGs) are critical yet often suffer from limited coverage compared to General Knowledge Graphs (GKGs). Existing ta

Parason: Revealing Subtask and Trial Parallelism in LLM Reasoning

arXiv:2608.24658v1 Announce Type: new Abstract: Scaling test-time reasoning has substantially improved the problem-solving ability of large language models (LLMs), but standard autoregressive decoding

Provenance Guided Incremental Learning Under Evolving Concept Definitions

arXiv:2608.23893v1 Announce Type: new Abstract: Learning systems deployed over long periods must adapt not only to statistical changes in incoming data, but also to revisions of the definitions that g

ReflCtrl: Controlling LLM Reflection Efficiently via Representation Engineering

arXiv:2512.13979v2 Announce Type: replace Abstract: Large reasoning models achieve strong performance on diverse tasks by producing extended chains of thought. Self-reflection, the ability to review a

RoG-DAgger: Rollout-Guided Post-Training for End-to-End Driving

arXiv:2608.24525v1 Announce Type: new Abstract: Recent end-to-end driving systems demonstrate strong performance on closed-loop benchmarks, yet are still predominantly trained on fixed expert-collecte

safetyarxiv-cs-ro

SatDL: Jointly Optimizing Data Redistribution and Training for Satellite-Based Distributed Learning

arXiv:2608.24516v1 Announce Type: cross Abstract: Satellite-based distributed learning promises to train machine-learning models directly in orbit using massive, globally dispersed sensor data, thereb

hardwarearxiv-cs-lg

Scalable and Versatile Identification for Hierarchical Structural Causal Models: A New Look at Project STAR

arXiv:2608.24500v1 Announce Type: cross Abstract: The STAR (Student-Teacher Achievement Ratio) experiment (1985, Tennessee, USA) is a landmark hierarchical dataset designed to assess the impact of cla

Scalable Question-Centric Text-to-Image Evaluation: Reliable Ranking, Fine-Grained Diagnosis, and Cost-Aware Routing

arXiv:2608.24112v1 Announce Type: new Abstract: Modern text-to-image (T2I) models often have similar total scores but different strengths, making practical selection difficult. Fine-grained benchmarks

hardwarearxiv-cs-ai

Serving Masked Diffusion LLMs: Characterization and Design Principles from Real Hardware

arXiv:2608.23807v1 Announce Type: new Abstract: Masked diffusion language models (dLLMs) can in principle generate text faster than autoregressive (AR) models, since they denoise many tokens at once.

hardwarearxiv-cs-ai

Spatiotemporal Distillation via Recurrent Bottlenecks for Aortic Tracking

arXiv:2608.23879v1 Announce Type: cross Abstract: Cardiac cine-MRI serves as a direct visual indicator of cardiovascular hemodynamics by capturing the continuous wall motion of the aorta. Quantifying

researcharxiv-cs-cv

TLXML: Task-Level Explanation of Meta-Learning via Influence Functions

arXiv:2501.14271v4 Announce Type: replace Abstract: Meta-learning enables models to rapidly adapt to new tasks by leveraging prior experience, but its adaptation mechanisms remain opaque, especially r

researcharxiv-cs-lg

TrAct: Bridging Robot Control and Visual Prediction with Visual Tracks

model-releasesarxiv-cs-ro

arXiv:2608.24101v1 Announce Type: new Abstract: Robot actions are inherently embodiment-specific and only weakly aligned with image-space visual changes, limiting their effectiveness as conditioning s