Introduces the TVR active viewpoint-matching task and TVRBench indoor simulation benchmark, where foundation models start at low single-digit success rates but reach 51.4% after visual-action SFT and multi-turn GRPO post-training.
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GPT-4o System Card
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abstract
GPT-4o is an autoregressive omni model that accepts as input any combination of text, audio, image, and video, and generates any combination of text, audio, and image outputs. It's trained end-to-end across text, vision, and audio, meaning all inputs and outputs are processed by the same neural network. GPT-4o can respond to audio inputs in as little as 232 milliseconds, with an average of 320 milliseconds, which is similar to human response time in conversation. It matches GPT-4 Turbo performance on text in English and code, with significant improvement on text in non-English languages, while also being much faster and 50\% cheaper in the API. GPT-4o is especially better at vision and audio understanding compared to existing models. In line with our commitment to building AI safely and consistent with our voluntary commitments to the White House, we are sharing the GPT-4o System Card, which includes our Preparedness Framework evaluations. In this System Card, we provide a detailed look at GPT-4o's capabilities, limitations, and safety evaluations across multiple categories, focusing on speech-to-speech while also evaluating text and image capabilities, and measures we've implemented to ensure the model is safe and aligned. We also include third-party assessments on dangerous capabilities, as well as discussion of potential societal impacts of GPT-4o's text and vision capabilities.
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- abstract GPT-4o is an autoregressive omni model that accepts as input any combination of text, audio, image, and video, and generates any combination of text, audio, and image outputs. It's trained end-to-end across text, vision, and audio, meaning all inputs and outputs are processed by the same neural network. GPT-4o can respond to audio inputs in as little as 232 milliseconds, with an average of 320 milliseconds, which is similar to human response time in conversation. It matches GPT-4 Turbo performance on text in English and code, with significant improvement on text in non-English languages, while
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representative citing papers
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CHASM: Unveiling Covert Advertisements on Chinese Social Media
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Algorithmic Recourse of In-Context Learning for Tabular Data
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K-FinHallu: A Hallucination Detection Benchmark for Multi-Turn RAG in Korean Finance
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Honest Lying: Understanding Memory Confabulation in Reflexive Agents
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RxEval: A Prescription-Level Benchmark for Evaluating LLM Medication Recommendation
RxEval benchmark shows frontier LLMs reach at most 46.10% exact match on prescription-level medication, dose, and route selection from real patient trajectories.
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Test-Time Learning with an Evolving Library
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Towards Order Fairness: Mitigating LLMs Order Sensitivity through Dual Group Advantage Optimization
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FORGE: Fragment-Oriented Ranking and Generation for Context-Aware Molecular Optimization
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Rethinking Importance Sampling in LLM Policy Optimization: A Cumulative Token Perspective
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From Static Analysis to Audience Dissemination: A Training-Free Multimodal Controversy Detection Multi-Agent Framework
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ResRL: Boosting LLM Reasoning via Negative Sample Projection Residual Reinforcement Learning
ResRL decouples shared semantics between positive and negative responses in LLM reinforcement learning via SVD-based projection residuals, outperforming baselines including NSR by up to 9.4% on math reasoning benchmarks.
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ParetoSlider: Diffusion Models Post-Training for Continuous Reward Control
ParetoSlider conditions diffusion models on continuous preference weights to approximate the full Pareto front, providing dynamic control over multi-objective rewards at inference time.
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Dynamic Tool Dependency Retrieval for Lightweight Function Calling
DTDR dynamically retrieves relevant tools by modeling dependencies from demonstrations and conditioning on the evolving agent plan, improving function calling success rates by 23-104% over static retrievers across benchmarks.
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TS-Reasoner: Domain-Oriented Time Series Inference Agents for Reasoning and Automated Analysis
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STRIDE: Learnable Stepwise Language Feedback for LLM Reasoning
STRIDE co-trains generator and verifier on outcome rewards alone to deliver learnable stepwise language feedback that redirects LLM reasoning trajectories and outperforms scalar-reward baselines.
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Understanding and Accelerating the Training of Masked Diffusion Language Models
Bell-shaped time sampling accelerates masked diffusion language model training by roughly 4x on LM1B by countering locality bias in language data.
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Overtrained, Not Misaligned
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Leveraging RAG for Training-Free Alignment of LLMs
RAG-Pref is a training-free RAG-based alignment technique that conditions LLMs on contrastive preference samples during inference, yielding over 3.7x average improvement in agentic attack refusals when combined with offline methods across five LLMs.
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FlashEvolve: Accelerating Agent Self-Evolution with Asynchronous Stage Orchestration
FlashEvolve accelerates LLM agent self-evolution via asynchronous stage orchestration and inspectable language-space staleness handling, reporting 3.5-4.9x proposal throughput gains over synchronous baselines on GEPA workloads.
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HyperEyes: Dual-Grained Efficiency-Aware Reinforcement Learning for Parallel Multimodal Search Agents
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SHARP: A Self-Evolving Human-Auditable Rubric Policy for Financial Trading Agents
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Uni-OPD: Unifying On-Policy Distillation with a Dual-Perspective Recipe
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MGDA-Decoupled: Geometry-Aware Multi-Objective Optimisation for DPO-based LLM Alignment
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Reasoning-targeted Jailbreak Attacks on Large Reasoning Models via Semantic Triggers and Psychological Framing
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From $P(y|x)$ to $P(y)$: Investigating Reinforcement Learning in Pre-train Space
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MapTab: Are MLLMs Ready for Multi-Criteria Route Planning in Heterogeneous Graphs?
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LLaDA2.0: Scaling Up Diffusion Language Models to 100B
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Compute as Teacher: Turning Inference Compute Into Reference-Free Supervision
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PiERN: Token-Level Routing for Integrating High-Precision Computation and Reasoning
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Rubrics as Rewards: Reinforcement Learning Beyond Verifiable Domains
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Vidar: Embodied Video Diffusion Model for Generalist Manipulation
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Exploring the Secondary Risks of Large Language Models
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Tokenizing Single-Channel EEG with Time-Frequency Motif Learning
TFM-Tokenizer learns a vocabulary of time-frequency motifs from single-channel EEG via a dual-path masked architecture and encodes signals into discrete tokens, reporting up to 11% Cohen's Kappa gains on benchmarks and 14% on ear-EEG sleep staging.
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SE-GA: Memory-Augmented Self-Evolution for GUI Agents
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Curriculum Learning-Guided Progressive Distillation in Large Language Models
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FLAME: Adaptive Mixture-of-Experts for Continual Multimodal Multi-Task Learning
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Uncertainty-Aware Exploratory Direct Preference Optimization for Multimodal Large Language Models
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Reasoning emerges from constrained inference manifolds in large language models
Reasoning in LLMs emerges from inference dynamics forming constrained low-dimensional manifolds that preserve non-degenerate information volume, rather than from compression alone.
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RunAgent: Interpreting Natural-Language Plans with Constraint-Guided Execution
RunAgent improves LLM reliability on structured plans by deriving constraints on the fly, using an agentic language with control flow, and dynamically selecting reasoning modes, outperforming baselines on Natural-plan and SciBench.
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Measure Twice, Click Once: Co-evolving Proposer and Visual Critic via Reinforcement Learning for GUI Grounding
A co-evolving proposer-critic RL framework improves GUI grounding accuracy by letting the model critique its own proposals rendered on screenshots.
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Learning to Trade Like an Expert: Cognitive Fine-Tuning for Stable Financial Reasoning in Language Models
A new fine-tuning framework with textbook-derived MCQs and simulation-based testing enables smaller open LLMs to show competitive, risk-aware financial trading behavior that outperforms baselines.
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Reward Hacking in the Era of Large Models: Mechanisms, Emergent Misalignment, Challenges
The paper introduces the Proxy Compression Hypothesis as a unifying framework explaining reward hacking in RLHF as an emergent result of compressing high-dimensional human objectives into proxy reward signals under optimization pressure.
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ECHO: Efficient Chest X-ray Report Generation with One-step Block Diffusion
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PRAGMA: Revolut Foundation Model
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From Synthesis to Clinical Assistance: A Strategy-Aware Agent Framework for Autism Intervention based on Real Clinical Dataset
ASDAgent generates synthetic ABA-strategy dialogues that match human therapist distributions (KL 0.083) and achieves 80% expert consistency, while its outputs improve small language models for therapeutic tasks.
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Fall into a Pit, Gain in a Wit: Cognitive-Guided Harmful Meme Detection via Misjudgment Risk Pattern Retrieval
PatMD improves harmful meme detection by retrieving misjudgment risk patterns to guide MLLMs, reporting 8.30% average F1 and 7.71% accuracy gains on 6,626 memes across 5 tasks.
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Failure Modes of Maximum Entropy RLHF
Derives SimPO from MaxEnt RL and reports that MaxEnt RL in online RLHF exhibits frequent overoptimization and unstable KL dynamics across scales, unlike stable KL-constrained baselines.
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If Concept Bottlenecks are the Question, are Foundation Models the Answer?
Empirical tests of VLM-CBMs show VLM supervision differs from expert annotations depending on task and that concept accuracy correlates weakly with quality metrics.
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Localization Boosting for Growth Markets: Mitigating Cross-Locale Behavioral Bias in Learning-to-Rank
Multi-objective LTR combining clicks, VLM labels, and locale boosting improves relevance and local content visibility across five growth markets.
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Perovskite-R1: a domain-specialized large language model for intelligent discovery of precursor additives and experimental design
A fine-tuned LLM called Perovskite-R1, built from curated perovskite literature and material libraries, proposes precursor additives and designs with some experimental validation showing improved stability and performance.