Chain-of-thought prompting, by including intermediate reasoning steps in few-shot examples, elicits strong reasoning abilities in large language models on arithmetic, commonsense, and symbolic tasks.
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Evaluating Large Language Models Trained on Code
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We introduce Codex, a GPT language model fine-tuned on publicly available code from GitHub, and study its Python code-writing capabilities. A distinct production version of Codex powers GitHub Copilot. On HumanEval, a new evaluation set we release to measure functional correctness for synthesizing programs from docstrings, our model solves 28.8% of the problems, while GPT-3 solves 0% and GPT-J solves 11.4%. Furthermore, we find that repeated sampling from the model is a surprisingly effective strategy for producing working solutions to difficult prompts. Using this method, we solve 70.2% of our problems with 100 samples per problem. Careful investigation of our model reveals its limitations, including difficulty with docstrings describing long chains of operations and with binding operations to variables. Finally, we discuss the potential broader impacts of deploying powerful code generation technologies, covering safety, security, and economics.
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- abstract We introduce Codex, a GPT language model fine-tuned on publicly available code from GitHub, and study its Python code-writing capabilities. A distinct production version of Codex powers GitHub Copilot. On HumanEval, a new evaluation set we release to measure functional correctness for synthesizing programs from docstrings, our model solves 28.8% of the problems, while GPT-3 solves 0% and GPT-J solves 11.4%. Furthermore, we find that repeated sampling from the model is a surprisingly effective strategy for producing working solutions to difficult prompts. Using this method, we solve 70.2% of ou
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DataComp-VLM benchmark shows instruction-heavy data mixing outperforms filtering for VLM training, with DCVLM-Baseline achieving 63.6% on 33 tasks for 8B models (+5.4pp over FineVision).
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LLM agents autonomously evolve the ABC logic synthesis tool by iteratively rewriting its source code to achieve better quality-of-results on standard benchmarks while preserving the original interface.
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DDIPE poisons LLM agent skills by embedding malicious logic in documentation examples, achieving 11.6-33.5% bypass rates across frameworks while explicit attacks are blocked, with 2.5% evading detection.
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Self-Distilled Trajectory-Aware Boltzmann Modeling: Bridging the Training-Inference Discrepancy in Diffusion Language Models
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EVOCHAMBER: Test-Time Co-evolution of Multi-Agent System at Individual, Team, and Population Scales
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TAD: Temporal-Aware Trajectory Self-Distillation for Fast and Accurate Diffusion LLM
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BadDLM: Backdooring Diffusion Language Models with Diverse Targets
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DUET: Optimize Token-Budget Allocation for Reinforcement Learning with Verifiable Rewards
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SpecBlock: Block-Iterative Speculative Decoding with Dynamic Tree Drafting
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Theoretical Limits of Language Model Alignment
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Echo-LoRA: Parameter-Efficient Fine-Tuning via Cross-Layer Representation Injection
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VOW: Verifiable and Oblivious Watermark Detection for Large Language Models
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Fast and Forgettable: A Controlled Study of Novices' Performance, Learning, Workload, and Emotion in AI-Assisted and Human Pair Programming Paradigms
Novices performed better and reported lower workload with GitHub Copilot than with human partners, but human partners produced more positive emotions and a smaller drop in retest performance after one week.
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BACE: LLM-based Code Generation through Bayesian Anchored Co-Evolution of Code and Test Populations
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Jamba: A Hybrid Transformer-Mamba Language Model
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Reflexion: Language Agents with Verbal Reinforcement Learning
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Reference-Sampled Boltzmann Projection for KL-Regularized RLVR: Target-Matched Weighted SFT, Finite One-Shot Gaps, and Policy Mirror Descent
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Uncertainty Quantification for LLM-based Code Generation
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Internalizing Curriculum Judgment for LLM Reinforcement Fine-Tuning
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CATS: Cascaded Adaptive Tree Speculation for Memory-Limited LLM Inference Acceleration
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Geometry Conflict: Explaining and Controlling Forgetting in LLM Continual Post-Training
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DARE: Difficulty-Adaptive Reinforcement Learning with Co-Evolved Difficulty Estimation
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Evaluating LLM-Generated Code: A Benchmark and Developer Study
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Semantic Voting: Execution-Grounded Consensus for LLM Code Generation
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VeriContest: A Competitive-Programming Benchmark for Verifiable Code Generation
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LLM Ghostbusters: Surgical Hallucination Suppression via Adaptive Unlearning
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MiniCPM-o 4.5: Towards Real-Time Full-Duplex Omni-Modal Interaction
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When to Vote, When to Rewrite: Disagreement-Guided Strategy Routing for Test-Time Scaling
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You Don't Need Public Tests to Generate Correct Code
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Co-Located Tests, Better AI Code: How Test Syntax Structure Affects Foundation Model Code Generation
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ELMoE-3D: Leveraging Intrinsic Elasticity of MoE for Hybrid-Bonding-Enabled Self-Speculative Decoding in On-Premises Serving
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AI-assisted modeling and Bayesian inference of unpolarized quark transverse momentum distributions from Drell-Yan data
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Weight Patching: Toward Source-Level Mechanistic Localization in LLMs
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GRACE: A Dynamic Coreset Selection Framework for Large Language Model Optimization
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Generating Verifiable Chain of Thoughts from Exection-Traces
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SWE-Bench Pro: Can AI Agents Solve Long-Horizon Software Engineering Tasks?
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Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling
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Enhancing the Reasoning Ability of Multimodal Large Language Models via Mixed Preference Optimization
Mixed Preference Optimization with the MMPR dataset boosts multimodal CoT reasoning, lifting InternVL2-8B to 67.0 accuracy on MathVista (+8.7 points) and matching the 76B model.
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Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena
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CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society
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Pion: A Spectrum-Preserving Optimizer via Orthogonal Equivalence Transformation
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Benchmarking EngGPT2-16B-A3B against Comparable Italian and International Open-source LLMs
EngGPT2MoE-16B-A3B matches or exceeds other Italian open-source LLMs on most international benchmarks while remaining competitive on ITALIC, though it trails some top international models.
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One Algorithm, Two Goals: Dual Scoring for Parameter and Data Selection in LLM Fine-Tuning
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ChipLingo: A Systematic Training Framework for Large Language Models in EDA
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LLM Reasoning Is Latent, Not the Chain of Thought
LLM reasoning is primarily mediated by latent-state trajectories rather than by explicit surface chain-of-thought outputs.
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JoyAI-LLM Flash: Advancing Mid-Scale LLMs with Token Efficiency
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