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
Mixed citation behavior. Most common role is background (65%).
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 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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representative citing papers
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).
Sumi is an openly released 7B parameter uniform diffusion language model pretrained from scratch on 1.5T tokens that matches autoregressive models on several benchmarks.
PCB-QA is the first QA benchmark for LLMs on printed circuit board designs, with Gemini 3 Flash Preview reaching 93% accuracy on a JSON textual representation.
TheoremBench is a Lean4 benchmark of classical theorems in main and premised forms that evaluates LLM provers on partial progress, coverage, and token efficiency rather than binary success on competition problems.
A new benchmark with cognitive traps shows frontier deep research agents achieve only 13-16% acceptance on expert consulting tasks under combined verifier and rubric criteria.
Mistletoe introduces a stealthy attack on speculative decoding that collapses acceleration by reducing average accepted length while preserving output semantics.
CIDR is a large-scale curated dataset of proprietary industrial source code repositories spanning 138 languages and 373 million lines of code, collected via formal agreements with industry partners.
PDEAgent-Bench is the first multi-metric, multi-library benchmark for AI-generated PDE solvers, evaluating executability, numerical accuracy, and efficiency across DOLFINx, Firedrake, and deal.II.
SimWorld Studio deploys an evolving coding agent to create adaptive 3D environments that co-evolve with embodied learners, delivering 18-point success-rate gains over fixed environments in navigation benchmarks.
PhysicianBench is a new benchmark of 100 physician-reviewed, execution-grounded tasks in live EHR environments where the best LLM agent reaches only 46% success and open-source models reach 19%.
AutoMat benchmark shows current LLM coding agents achieve at most 54.1% success when reproducing computational materials science claims from papers.
MLLMs exhibit a Mirage effect by bypassing circuit diagrams in favor of header semantics for Verilog generation; VeriGround with identifier anonymization and D-ORPO training reaches 46% Functional Pass@1 while refusing blank images at >92%.
StabilizerBench is a new benchmark for evaluating AI agents on generating, optimizing, and making fault-tolerant stabilizer circuits for quantum error correction, with efficient verification and multi-tier scoring.
NLI autonomously discovers a vocabulary of primitive operations and interprets variable-length programs via a neural executor, allowing end-to-end training and gradient-based test-time adaptation that outperforms prior methods on combinatorial generalization tasks.
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.
FermiLink is a unified AI agent framework that automates multidomain scientific simulations via separated package knowledge bases and a four-layer progressive disclosure mechanism, reproducing 56% of target figures in benchmarks and generating research-grade results on unpublished problems.
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.
Derives an exact unbiased policy gradient for RL post-training of diffusion LLMs via entropy-guided step selection and one-step denoising rewards, achieving state-of-the-art results on coding and logical reasoning benchmarks.
MCP-Atlas is a new benchmark with 1000 tasks on production MCP servers that uses claim-level scoring to evaluate LLM agents on realistic multi-step tool-use competency.
CritPt benchmark shows state-of-the-art LLMs reach only 5.7% average accuracy on full-scale unpublished physics research tasks, rising to about 10% with coding tools.
AIDev is a new open dataset of 456k AI-agent pull requests showing agents submit code faster than humans but with lower acceptance rates and simpler changes.
LLaDA is a scalable diffusion-based language model that matches autoregressive LLMs like LLaMA3 8B on tasks and surpasses GPT-4o on reversal poem completion.
Molmo VLMs trained on newly collected PixMo open datasets achieve state-of-the-art performance among open-weight models and surpass multiple proprietary VLMs including Claude 3.5 Sonnet and Gemini 1.5 Pro.
citing papers explorer
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PCB-QA: Evaluating LLMs over the First Printed Circuit Board Design Question-Answer Dataset
PCB-QA is the first QA benchmark for LLMs on printed circuit board designs, with Gemini 3 Flash Preview reaching 93% accuracy on a JSON textual representation.
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Autonomous Evolution of EDA Tools: Multi-Agent Self-Evolved ABC
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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Reward-Weighted On-Policy Distillation with an Open Property-Equivalence Verifier for NL-to-SVA Generation
Reward-Weighted On-Policy Distillation with an open property-equivalence verifier produces a 7B model that surpasses prior SOTA on NL-to-SVA generation across pass@1/5/10 metrics.
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A Full-Stack Performance Evaluation Infrastructure for 3D-DRAM-based LLM Accelerators
ATLAS is the first silicon-validated simulation framework for 3D-DRAM LLM accelerators, achieving under 8.57% error and over 97% correlation with real hardware while supporting design exploration.
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Synthesis-in-the-Loop Evaluation of LLMs for RTL Generation: Quality, Reliability, and Failure Modes
LLM evaluation for RTL generation identifies three performance tiers with frontier models reaching high synthesis quality and reveals systematic failure differences between proprietary and open models.
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FVRuleLearner: Operator-Level Reasoning Tree (OP-Tree)-Based Rules Learning for Formal Verification
FVRuleLearner introduces an Operator Reasoning Tree to learn operator-specific rules that improve natural-language to SystemVerilog assertion generation, raising syntax correctness by 3.95% and functional correctness by 31.17% over baselines.
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FT-Pilot: Automated Fault-Tolerant RTL Rewriting via Vulnerability-Guided LLMs
FT-Pilot is a GNN-guided LLM framework that identifies vulnerable RTL assets and performs automated fault-tolerant code rewriting to produce correct, synthesizable hardened designs with reduced soft-error rates.
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GR-Evolve: Design-Adaptive Global Routing via LLM-Driven Algorithm Evolution
GR-Evolve applies LLM-driven code evolution to global routing, reporting up to 8.72% post-detailed-routing wirelength reduction on seven benchmarks across three technology nodes.
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AnalogMaster: Large Language Model-based Automated Analog IC Design Framework from Image to Layout
AnalogMaster applies large language models to end-to-end analog IC design automation, converting images to netlists and optimizing parameters to achieve 92.9% Pass@1 and 99.9% Pass@5 success on 15 test circuits using GPT-5.
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Configuration Over Selection: Hyperparameter Sensitivity Exceeds Model Differences in Open-Source LLMs for RTL Generation
Hyperparameter configuration in open-source LLMs for RTL generation produces up to 25.5% intra-model pass-rate variation on VerilogEval and RTLLM, exceeding inter-model spreads by 5x with near-zero correlation in optimal settings across benchmarks.
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InCoder-32B-Thinking: Industrial Code World Model for Thinking
InCoder-32B-Thinking uses error-feedback synthesized thinking traces and a code world model to reach top open-source scores on general and industrial code benchmarks including 81.3% on LiveCodeBench and 84.0% on CAD-Coder.
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CHICO-Agent: An LLM Agent for the Cross-layer Optimization of 2.5D and 3D Chiplet-based Systems
CHICO-Agent uses LLM agents with a knowledge base to find lower-cost configurations for 2.5D/3D chiplet systems than simulated annealing while providing an interpretable audit trail.
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VerilogCL: A Contrastive Learning Framework for Robust LLM-Based Verilog Generation
VerilogCL applies contrastive learning with minimal-error data pairs and a proactive screening module to improve compilation success and functional correctness of 7B LLM-generated Verilog over open-source and commercial baselines on VerilogEval and RTLLM benchmarks.
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Understanding Inference-Time Token Allocation and Coverage Limits in Agentic Hardware Verification
Domain-specialized LLM agents for hardware verification close 95-99% coverage using 4-13x fewer tokens and 2-4x faster convergence than general-purpose agents by reallocating tokens toward coverage-directed reasoning.
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VeriInteresting: An Empirical Study of Model Prompt Interactions in Verilog Code Generation
Empirical study identifies patterns in how model classes respond to structured prompts, optimization, and other techniques across two Verilog benchmarks.
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Surveying GenAI-based Automation in Printed Circuit Board Design and Test
Survey of GenAI in PCB design lifecycle presenting taxonomy, technical challenges, and research directions.