Search: “reasoning”

From Pixel to Coding: Evaluating the Figure Reproduction Capabilities of MLLMs

model-releasesarxiv-cs-cv

arXiv:2610.10066v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities in both visual understanding and code generation. However, existing b

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From Uncertainty to Action: Learning to Steer LLM Agents

DGX agent

arXiv:2610.09115v1 Announce Type: cross Abstract: Steering an LLM agent means deciding whether to correct it, at which step, and with which mechanism. Uncertainty is often used to decide when to corre

model-releasesarxiv-cs-cl

8 Oct 2026

Generative AI for Autonomous Driving: Frontiers and Opportunities

arXiv:2505.08854v2 Announce Type: replace Abstract: Generative Artificial Intelligence (GenAI) constitutes a transformative technological wave that reconfigures industries through its unparalleled cap

safetyarxiv-cs-cv

LayerRoPE: Dynamic Depth-wise Magnitude & Angular Superposition

arXiv:2610.09179v1 Announce Type: cross Abstract: As data propagates through a Transformer, the norm of its hidden states grows by orders of magnitude with depth, a phenomenon framed as 'curse of dept

researcharxiv-cs-cl

Multi-Label Topic Assignment via LLM Distillation: A Comparative Analysis of Generative vs. Discriminative Student Models

arXiv:2610.09063v1 Announce Type: cross Abstract: Multi-label topic assignment for user-generated content (UGC) -- including product reviews and buyer-seller conversations -- poses unique scalability

SearchWorld: Spatial Value-Grounded Imagination for UAV Object Search via World Models

arXiv:2610.09335v1 Announce Type: cross Abstract: Autonomous unmanned aerial vehicle (UAV) object search involves a closed loop of perception, decision-making, and action under partial observability.

safetyarxiv-cs-lg

Tokka-Bench: Evaluating Tokenizers Across 100 Natural and 20 Programming Languages

arXiv:2610.08794v1 Announce Type: new Abstract: Large language models rely on subword tokenizers whose quality varies across languages, yet no standardized multi-metric framework exists for broad comp

A Systematic Investigation of Bias in Large Language Models for Advertising Relevance

arXiv:2610.07544v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to judge how well an advertisement matches a query, but the fairness of these judgments has received

Agentic Design Space Exploration for Joint Hardware Configuration Selection and Mapping of AI Inference Workloads on Heterogeneous Edge SoCs

arXiv:2610.07191v1 Announce Type: cross Abstract: Modern edge Systems-on-Chip (SoCs) integrate heterogeneous processing units (PUs) such as CPUs, GPUs, and NPUs, each with distinct performance and ene

AMBER: Training Long-Horizon Web Agents through Append-Only Memory

arXiv:2610.07118v1 Announce Type: new Abstract: Modern language-model agents increasingly interact with external environments over long-horizon, multi-step trajectories, where the accumulated interact

Artemis: Geometry-Grounded Multi-Agent Driving World Models with Shared 3D State and Progressive Memory Update

arXiv:2610.07031v1 Announce Type: new Abstract: Recent video world models have witnessed the paradigm shift from single-agent to multi-agent involvements, which can reveal more complicated dynamics an

agentsarxiv-cs-cv

Calibrated Answers About Randomized Trials From a 4-Billion-Parameter Open Model: A Registered Test and a License-Clean Release

arXiv:2610.07019v1 Announce Type: new Abstract: Fiorillo v0.5 is an open model that answers typed questions with a probability for each answer. Its main specialist reads a randomized trial's article,

CheckerBench: Can Long-Horizon Agents Synthesize Static-Analysis Checkers?

arXiv:2610.07557v1 Announce Type: cross Abstract: Static-analysis checker synthesis requires agents to interpret a defect specification, inspect a repository, implement analyzer-specific logic, and re

CoDe-LoRA: Mitigating the Orthogonality Dilemma in Continual Learning of LLMs via Knowledge Consolidation and Decoupling

arXiv:2610.08312v1 Announce Type: new Abstract: Continual learning (CL) is essential for Large Language Models (LLMs) to sequentially adapt to evolving tasks. To mitigate catastrophic forgetting, rece

CueRator: Agentic Search for Symbolic Rules to Adapt Frozen Multimodal Encoders

arXiv:2610.07868v1 Announce Type: new Abstract: Large language model agents have been used to search over symbolic structures such as programs and equations. We propose CueRator, an agentic framework

safetyarxiv-cs-cv

DecepEval: A Benchmark for Evaluating Deception in LLM Agents

model-releasesarxiv-cs-lg

arXiv:2610.07967v1 Announce Type: new Abstract: As large language model (LLM) agents become increasingly autonomous, they may pursue task performance through deception, raising concerns about their re

Decide Before You Look: Learning Which Retrieved Memories Deserve Pixels

arXiv:2610.07984v1 Announce Type: cross Abstract: Multimodal assistants answer questions from long-term memories that contain images. After retrieval, each retrieved image reaches the answering model

DeltaTTT: Layerwise Optimization for Nonlinear Recurrent Memory

arXiv:2610.08553v1 Announce Type: cross Abstract: Sequential test-time training adapts a memory network through successive updates, each computing an inner-loop gradient based on the network's previou

Does an Agent's History Tell You When Compaction Will Hurt? A Modest, Bounded Effect on the TRACE Paired-Replay Corpus

arXiv:2610.08722v1 Announce Type: new Abstract: Many long-horizon agents compact their context on a global rule, usually a token budget, blind to what the agent was doing. We ask whether the agent's r

EC-RAG: Event Chain Retrieval-Augmented Generation for Long Video Understanding

arXiv:2610.08674v1 Announce Type: new Abstract: Current large video-language models (LVLMs) still face challenges when dealing with long videos, mainly because frames are often processed independently

researcharxiv-cs-cv

Emoception: Selective Affective Layer Fine-Tuning of Video Vision Transformers for Player Arousal Change Recognition From Gameplay Footage

arXiv:2610.07603v1 Announce Type: cross Abstract: This article proposes Selective Affective Layer Fine-Tuning (SALFT), an efficient adaptation framework for Video Vision Transformers in player arousal

EMODE: Dynamic Para-Semantic Experts for Emotion-Aware Speech Language Modeling

arXiv:2610.06956v1 Announce Type: new Abstract: Large speech language models have demonstrated strong capabilities in unified cross-modal understanding and generation, yet paralinguistic cues, especia

Evaluating Escalation Signals for LLM Routing: Targets, Controls, and Five Ways to Fool Yourself

arXiv:2610.07354v1 Announce Type: new Abstract: Deciding when to escalate a query from a small language model to a larger one requires a cheap signal that predicts, before the large model is called, w

Explainable Failure Prediction and Prevention in Maritime

arXiv:2610.08363v1 Announce Type: new Abstract: Maritime systems operate in highly dynamic environments where unexpected equipment failures can compromise safety, reliability, and operational efficien

Applications

FluidPD: In-Place Elasticity for SLO-Aware Prefill-Decode Disaggregated LLM Serving

arXiv:2610.06917v1 Announce Type: new Abstract: Prefill-decode disaggregation is becoming a common architecture for LLM serving because it separates two phases with distinct execution patterns and SLO

applicationsarxiv-cs-ai

Forensic Reserve: Eliciting Latent Knowledge for Image Forgery Detection

model-releasesarxiv-cs-cv

arXiv:2610.08639v1 Announce Type: new Abstract: As generated images become increasingly realistic, reliable forgery detection is essential for maintaining trust in visual information. However, existin

Fork-and-Flush: Escaping Idea Basins in Autoresearch Agents

arXiv:2610.07447v1 Announce Type: new Abstract: Autoresearch agents tackle open-ended problems by repeatedly proposing candidate solutions, evaluating them, and using feedback to guide subsequent expe

agentsarxiv-cs-lg

How Much Do LLM-as-a-Judge Design Choices Matter? A Systematic Comparison of Prompt Designs, Rating Scales, and Models

arXiv:2610.05094v1 Announce Type: cross Abstract: Researchers increasingly use Large Language Models as judges (LLM-as-a-judge) to evaluate model outputs. Yet there are no standards for how to design

How Much Planning Is Enough? Reducing Search and Computation in World-Model Planning

arXiv:2610.08350v1 Announce Type: cross Abstract: Visual world models enable goal-directed control through decision-time action search, but their deployment efficiency is often limited by conservative

Improving Synthetic Data Generation for Argument Mining via Adversarial Reinforcement Learning

arXiv:2610.07699v1 Announce Type: cross Abstract: Argument Mining (AM) is fundamentally constrained by the scarcity of high-quality structure-annotated datasets. While LLMs have shown promise in synth

Intersectional Fairness via Mixed-Integer Optimization

arXiv:2601.19595v2 Announce Type: replace-cross Abstract: The deployment of Artificial Intelligence in high-risk domains, such as finance and healthcare, necessitates models that are both fair and tra

Tutorials

Learning Explainable Representations of Complex Game-playing Strategies

arXiv:2610.07638v1 Announce Type: new Abstract: As part of learning to play complex games, human players develop develop abstractions for concepts and strategies of gameplay consistent with game rules

tutorialsarxiv-cs-ai

Navier-Stokes lost in translation: Why Lean verification of AI autoformalisation does not guarantee correct natural language proofs

arXiv:2610.08144v1 Announce Type: cross Abstract: Autoformalisation is increasingly used to verify mathematical texts, including those generated by AI, as in OpenAI's announced proof of blow-up of sol

Local Ai

OpenWAM: An Open Framework for Composable World-Action Models

arXiv:2610.07922v1 Announce Type: cross Abstract: World-action models (WAMs) couple future prediction with robot control, yet existing systems often vary the video backbone, interaction structure, sup

local-aiarxiv-cs-cv

Partially Observable Zero-shot coordination by Predicting Intention of Partner

arXiv:2610.08142v1 Announce Type: new Abstract: Zero-shot coordination in embodied settings requires acting while the partner is intermittently out of view, leaving existing methods with ambiguous par

Persistent Memory in Multi-Agent LLM Inference: What It Costs, What It Buys, and When You Can Tell

arXiv:2610.07782v1 Announce Type: new Abstract: Decomposing long-context inference across cooperating agents bounds the active KV cache per call rather than total evidence, which matters when KV-cache

Quadratic Direct Forecast for Training Multi-Step Time-Series Forecast Models

arXiv:2511.00053v2 Announce Type: replace-cross Abstract: The design of learning objectives is central to training time-series forecasting models. Existing learning objectives such as mean squared err

Quantifying the Gap between Understanding and Generation within Unified Multimodal Models

arXiv:2602.02140v2 Announce Type: replace Abstract: Recent advances in unified multimodal models (UMM) have demonstrated remarkable progress in both understanding and generation tasks. However, whethe

Readout Stability in Prefill-Only Decision Models:Zero-Label Prediction and Inference-Time Compute Allocation

arXiv:2610.07716v1 Announce Type: new Abstract: Prefill-only decision models inspired by the Jev model score every candidate in a menu during a single forward pass and never decode, which makes one ca

researcharxiv-cs-cl

ReGraph: A Computational Account of Emergent Generalization in the 'what' and 'where' Dual Visual Streams

arXiv:2610.07962v1 Announce Type: cross Abstract: Where generalization capacity--the ability to extract context-invariant relational structures--first emerges remains a central question in AI and neur

Representation Bias, Correction Transfer, and Resolution Sensitivity in Three-Dimensional Mitochondrial Morphometry

arXiv:2610.07582v1 Announce Type: new Abstract: Quantitative imaging pipelines can produce precise but systematically different measurements of the same object. We present an empirical reliability ass

Rethinking Visual Provenance: Detection and Watermarking Across Direct Visual Generation and LLM-Driven Code Rendering

model-releasesarxiv-cs-cv

arXiv:2610.08137v1 Announce Type: cross Abstract: AI systems create images and videos with image/video generation models or by writing code and graphics descriptions that are then rendered. These rout

Retrieval Is Not Enough: Refreshing Memory for Frozen Time-Series Forecasters

arXiv:2610.07834v1 Announce Type: new Abstract: Retrieval-augmented time-series forecasting uses the continuations of historical segments similar to the current context as references for a forecaster.

safetyarxiv-cs-lg

Rule-Based Languages for Neurosymbolic AI

arXiv:2610.07313v1 Announce Type: new Abstract: Logic programming is increasingly used as the symbolic component of neurosymbolic AI systems. We survey the main rule-based languages in this setting, n

SAFESHIELD: A Decision-Organization Framework for Deployment-Time Safety of Small Language Models

arXiv:2610.07276v1 Announce Type: cross Abstract: Deployment-time safety of language models is commonly implemented through runtime guardrails such as input moderation, routing, retrieval verification

SENSE: State-aware Emotion Navigation Storytelling Engine

arXiv:2610.07666v1 Announce Type: cross Abstract: This paper presents SENSE, a state-aware framework for generating playable branching visual novels with multi-track emotional navigation. Integrating

SPECTRUM: Proximal Spectral Modulation for Looped Self-Distillation

arXiv:2610.07237v1 Announce Type: new Abstract: A model that learns from its own outputs inherits more than their correctness: it inherits which solutions it produces. We formulate Looped Self-Distill

Stabilizing Off-Policy Training for Long-Horizon LLM Agent via Turn-Level Importance Sampling and Clipping-Triggered Normalization

arXiv:2511.20718v3 Announce Type: replace-cross Abstract: Reinforcement learning (RL) algorithms such as PPO and GRPO are widely used to train large language models (LLMs) for multi-turn agentic tasks