Tag
Evo is an open-source tool that provides semi-autonomous agents to optimize codebases through parallel experimentation, using tree search and multiple subagents to autonomously discover and improve metrics.
LEAP is an agentic framework that enables general-purpose LLMs to achieve state-of-the-art performance in formal theorem proving in Lean, solving all 12 problems from the 2025 Putnam Competition and boosting formal solve rates from below 10% to 70% on a new benchmark (Lean-IMO-Bench), surpassing specialized systems.
MapAgent is an industrial-grade agentic framework that combines vision-language processing with constraint-aware reasoning to automatically produce specification-compliant lane-level maps, achieving over 95% automation in Baidu Maps for more than 360 cities.
MOSAIC introduces a structured agentic framework for automated data science that uses memory-grounded model selection and workflow construction, validated on financial time-series tasks. It outperforms AutoML and agentic baselines.
HypoAgent is an agentic framework for interactive abductive hypothesis generation over knowledge graphs, integrating three agents to handle evolving user intents and fine-grained diagnosis, achieving state-of-the-art performance.
ACO System is an open-source multi-agent framework that autonomously manages the software development pipeline from GitHub Issue to merged PR using six specialized AI agents, with a deterministic architect gate to prevent bad PRs.
This paper introduces RACE-Sched, an asynchronous agentic framework that decouples real-time reactive scheduling from deliberative LLM-based reasoning to handle dynamic job shop scheduling problems, achieving superior performance over DRL and other baselines.
Introduces Multi-Agent Residual In-Context Learning (MARICL), an agentic framework that uses LLM agents to analyze residuals from a base model on tabular data, hypothesize missing structure, and produce explicit correction terms via textual gradient optimization. Across nine benchmarks, MARICL consistently improves over its base model and demonstrates mechanistic generalization in cell-free protein predictions.
Research Math Agents (RMA) is an agentic framework for automated reasoning on research-level mathematical problems, achieving state-of-the-art results on the First Proof benchmark by solving 8 out of 10 problems, outperforming strong baselines like GPT-5.2R and Aletheia.
ORACLE is a new agentic framework for early scam anticipation from streaming app usage trajectories. It uses a self-evolving context manager and on-policy self-distillation to detect scams from partial observations over multiple apps and days.
ClinSeekAgent is an automated agentic framework that enables large language models to actively acquire and synthesize multimodal clinical evidence from raw data sources, improving decision-making accuracy in both text-only and multimodal tasks. It introduces the ClinSeek-Bench benchmark and a distilled model ClinSeek-35B-A3B that achieves strong performance on agentic clinical reasoning.
RTL-BenchMT is an agentic framework that automatically identifies and revises flawed cases and detects overfitting in RTL generation benchmarks, reducing human maintenance effort in EDA research.
Lean Refactor presents a retrieval-augmented agentic framework for multi-objective, controllable, and version-robust refactoring of Lean proofs, achieving significant compression and compilation-time reduction.
A novel MLLM-based agentic framework called Code-as-Room generates 3D indoor rooms by converting top-down images into executable Blender code through a structured execution harness with cross-stage memory to maintain context.
Google's Nexus paper proposes an agentic framework that incorporates contextual events alongside numerical data for time series forecasting, achieving an 86.6% MAPE reduction on Zillow tests compared to direct chain-of-thought prompting.
This paper presents an agentic framework that uses coding agents to generate physically plausible world simulations from natural language prompts, outperforming video-based models in physical accuracy and instruction fidelity.
GraphBit is a graph-based agentic framework that uses deterministic DAG orchestration with a Rust engine to eliminate hallucinations and infinite loops. It achieves 67.6% accuracy on GAIA benchmarks with zero framework-induced errors and low latency.
Proposes an agentic framework using LangChain agents for population-scale mental health screening, focusing on depression detection from clinical transcripts. The framework incrementally locks validated stages and uses proxy-guided evaluation to ensure trustworthiness and adaptability.
Nexus introduces a multi-agent framework that decomposes time series forecasting into specialized stages, integrating numerical patterns and contextual information using LLMs, achieving state-of-the-art results on benchmarks.
Physics-intern is an agentic framework for theoretical physics that improves Gemini 3.1 Pro's performance on the CritPt benchmark from 17.7% to 31.4%, achieving a new state-of-the-art.