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ENSEMBITS: an alphabet of protein conformational ensembles

(How) Do Large Language Models Understand High-Level Message Sequence Charts?

Query-Conditioned Test-Time Self-Training for Large Language Models

Constitutional Governance in Metric Spaces

Inducing Overthink: Hierarchical Genetic Algorithm-based DoS Attack on Black-Box Large Language Reasoning Models

LeanSearch v2: Global Premise Retrieval for Lean 4 Theorem Proving

MLGIB: Multi-Label Graph Information Bottleneck for Expressive and Robust Message Passing

Watermarking Should Be Treated as a Monitoring Primitive

Does language matter for spoken word classification? A multilingual generative meta-learning approach

Scaling few-shot spoken word classification with generative meta-continual learning

Context Training with Active Information Seeking

WriteSAE: Sparse Autoencoders for Recurrent State

Learning, Fast and Slow: Towards LLMs That Adapt Continually

Beyond GRPO and On-Policy Distillation: An Empirical Sparse-to-Dense Reward Principle for Language-Model Post-Training

Detecting overfitting in Neural Networks during long-horizon grokking using Random Matrix Theory

A New Technique for AI Explainability using Feature Association Map

TokenRatio: Principled Token-Level Preference Optimization via Ratio Matching

Boosting Omni-Modal Language Models: Staged Post-Training with Visually Debiased Evaluation

GEAR: Granularity-Adaptive Advantage Reweighting for LLM Agents via Self-Distillation

Overcoming Dynamics-Blindness: Training-Free Pace-and-Path Correction for VLA Models

Predictive Maps of Multi-Agent Reasoning: A Successor-Representation Spectrum for LLM Communication Topologies

LoKA: Low-precision Kernel Applications for Recommendation Models At Scale

Prompt-Activation Duality: Improving Activation Steering via Attention-Level Interventions

Pretraining large language models with MXFP4 on Native FP4 Hardware

ShadowMerge: A Novel Poisoning Attack on Graph-Based Agent Memory via Relation-Channel Conflicts

Evolutionary Ensemble of Agents

Geometrically Constrained Stenosis Editing in Coronary Angiography via Entropic Optimal Transport

AgentForesight: Online Auditing for Early Failure Prediction in Multi-Agent Systems

Trapping Attacker in Dilemma: Examining Internal Correlations and External Influences of Trigger for Defending GNN Backdoors

Normalization Equivariance for Arbitrary Backbones, with Application to Image Denoising

Flow-OPD: On-Policy Distillation for Flow Matching Models

One Token Per Frame: Reconsidering Visual Bandwidth in World Models for VLA Policy

Toward Privileged Foundation Models:LUPI for Accelerated and Improved Learning

Predict-then-Diffuse: Adaptive Response Length for Compute-Budgeted Inference in Diffusion LLMs

Motion-Aware Caching for Efficient Autoregressive Video Generation

ClawGym: A Scalable Framework for Building Effective Claw Agents

SIEVES: Selective Prediction Generalizes through Visual Evidence Scoring

Quantifying and Mitigating Self-Preference Bias of LLM Judges

Quotient-Space Diffusion Models

Image Generators are Generalist Vision Learners

RoboWM-Bench: A Benchmark for Evaluating World Models in Robotic Manipulation

Evaluating Adaptive Personalization of Educational Readings with Simulated Learners

UniMamba: A Unified Spatial-Temporal Modeling Framework with State-Space and Attention Integration

RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies

ECHO: Elastic Speculative Decoding with Sparse Gating for High-Concurrency Scenarios

PinpointQA: A Dataset and Benchmark for Small Object-Centric Spatial Understanding in Indoor Videos

LLMs Should Express Uncertainty Explicitly

A Security Analysis of the OpenClaw AI Agent Framework

OneSearch-V2: The Latent Reasoning Enhanced Self-distillation Generative Search Framework

Procedural Refinement by LLM-driven Algorithmic Debugging for ARC-AGI-2

Residual Stream Duality in Modern Transformer Architectures

M$^2$RNN: Non-Linear RNNs with Matrix-Valued States for Scalable Language Modeling

Reinforcement Learning for Diffusion LLMs with Entropy-Guided Step Selection and Stepwise Advantages

TERMINATOR: Learning Optimal Exit Points for Early Stopping in Chain-of-Thought Reasoning

Adapting Dijkstra for Buffers and Unlimited Transfers

Neural Field Thermal Tomography: A Differentiable Physics Framework for Non-Destructive Evaluation

V2M-Zero: Zero-Pair Time-Aligned Video-to-Music Generation

Gradient Iterated Temporal-Difference Learning

Vibe Code Bench: Evaluating AI Models on End-to-End Web Application Development

Robometer: Scaling General-Purpose Robotic Reward Models via Trajectory Comparisons

Decoupling Stability and Plasticity for Multi-Modal Test-Time Adaptation

MPU: Towards Secure and Privacy-Preserving Knowledge Unlearning for Large Language Models

Grokking Finite-Dimensional Algebra

CUICurate: A GraphRAG-based Framework for Automated Clinical Concept Curation for NLP applications

MALLVI: A Multi-Agent Framework for Integrated Generalized Robotics Manipulation

Artificial Intelligence Specialization in the European Union: Underexplored Role of the Periphery at NUTS-3 Level

Numerical exploration of the range of shape functionals using neural networks

Finding Interpretable Prompt-Specific Circuits in Language Models

ArGEnT: Arbitrary Geometry-encoded Transformer for Operator Learning

Krause Synchronization Transformers

Proximal Action Replacement for Behavior Cloning Actor-Critic in Offline Reinforcement Learning

VLRS-Bench: A Vision-Language Reasoning Benchmark for Remote Sensing

GhostCite: A Large-Scale Analysis of Citation Validity in the Age of Large Language Models

Boosting LLM Reasoning via Human-Inspired Reward Shaping

PaAno: Patch-Based Representation Learning for Time-Series Anomaly Detection

Neural Signals Generate Clinical Notes in the Wild

L2R: Low-Rank and Lipschitz-Controlled Routing for Mixture-of-Experts

OPT-Engine: Benchmarking the Limits of LLMs in Optimization Modeling via Complexity Scaling

Teaching and Evaluating LLMs to Reason About Polymer Design Related Tasks

The Multi-View Paradigm Shift in MRI Radiomics: Predicting MGMT Methylation in Glioblastoma

LangPrecip: Language-Aware Multimodal Precipitation Nowcasting

Achieving Approximate Symmetry Is Exponentially Easier than Exact Symmetry

Why Goal-Conditioned Reinforcement Learning Works: Relation to Dual Control

AI-Driven Optimization under Uncertainty for Mineral Processing Operations

fMRI-LM: Towards a Universal Foundation Model for Language-Aligned fMRI Understanding

A cross-species neural foundation model for end-to-end speech decoding

How Learning Rate Decay Wastes Your Best Data in Curriculum-Based LLM Pretraining

Optimizing PyTorch Inference with LLM-Based Multi-Agent Systems

Chinese Short-Form Creative Content Generation via Explanation-Oriented Multi-Objective Optimization

Descriptor: Distance-Annotated Traffic Perception Question Answering (DTPQA)

Moral Susceptibility and Robustness under Persona Role-Play in Large Language Models

From Ranking to Reasoning: Explainable Web API Recommendation via Semantic Reasoning

Higher-order Linear Attention

A Tutorial on Cognitive Biases in Agentic AI-Driven 6G Autonomous Networks

Seeing Through the Brain: New Insights from Decoding Visual Stimuli with fMRI

AMiD: Knowledge Distillation for LLMs with $\alpha$-mixture Assistant Distribution

VER: Vision Expert Transformer for Robot Learning via Foundation Distillation and Dynamic Routing

TiTok: Transfer Token-level Knowledge via Contrastive Excess to Transplant LoRA

The Pitfalls of KV Cache Compression

AVEX: What Matters for Animal Vocalization Encoding

Combining Deep Reinforcement Learning and Search with Generative Models for Game-Theoretic Opponent Modeling

Created by

Haebom

저자

Zun Li, Marc Lanctot, Kevin R. McKee, Luke Marris, Ian Gemp, Daniel Hennes, Paul Muller, Kate Larson, Yoram Bachrach, Michael P. Wellman

개요

본 논문은 심층 게임 이론 강화 학습을 이용한 확장 가능하고 일반적인 다중 에이전트 훈련 체계를 제시합니다. 기존의 상대 모델링 방법들이 도메인 특정 휴리스틱과 확장성이 낮은 최적 반응 알고리즘에 의존하는 것과 달리, Monte-Carlo Tree Search (MCTS) 기반의 생성적 최적 반응(GenBR) 알고리즘을 제안합니다. GenBR은 학습된 심층 생성 모델을 사용하여 계획 중 세계 상태를 샘플링하여 대규모 불완전 정보 도메인에서도 확장 가능하며 다양한 다중 에이전트 알고리즘에 플러그 앤 플레이 방식으로 적용 가능합니다. Policy Space Response Oracles (PSRO) 프레임워크 하에서 반복적인 게임 이론적 추론과 집단 기반 훈련을 통해 오프라인 상대 모델을 자동으로 생성하고, 협상 이론 기반의 해결 개념을 사용하여 파레토 최적 프런티어 근처의 프로파일을 식별하여 상대 혼합 모델을 구축합니다. 또한, GenBR을 통해 온라인 상대 모델을 지속적으로 업데이트하고 게임 플레이 중 반응합니다. Deal-or-No-Deal 협상 게임을 통해 인간 참가자와의 행동 연구를 수행하여 제안된 방법의 효과를 검증합니다.

시사점, 한계점

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시사점:

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대규모 불완전 정보 게임에서 확장 가능한 상대 모델링 방법 제시

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심층 생성 모델 기반 MCTS를 이용한 효율적인 최적 반응 알고리즘 (GenBR) 제안

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PSRO 프레임워크와 협상 이론 기반의 상대 모델 구축 및 업데이트 전략 제시

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인간과의 협상 게임에서 인간 수준의 사회적 복지 및 내쉬 협상 점수 달성

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온라인 베이지안 상대 예측 가능

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한계점:

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제안된 방법의 일반화 성능에 대한 추가적인 연구 필요

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특정 게임 환경에 대한 의존성 및 다른 게임 환경으로의 적용 가능성 검증 필요

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더욱 복잡하고 다양한 상대 전략에 대한 로버스트성 평가 필요

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계산 비용 및 학습 시간에 대한 분석 필요

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