agentic-framework

ASIRF is an agentic framework that retrieves domain-specific definitions for sensitive information redaction at inference time, achieving higher recall than trained classifiers across various domains with minimal expert input.

SpecOpt introduces a molecular design task to improve drug binding specificity by making targeted modifications to existing compounds using an agentic framework with residue-aware contacts, showing improvement for 84.8% of compounds in a benchmark.

This paper proposes FairCompressAgent, an agentic framework that uses a language-model planner to optimize fairness-aware model compression for FPGA deployment, balancing accuracy, fairness, and efficiency.

The paper proposes CLEAR, an agentic framework for cross-source evidence adjudication to improve large language models in medicine by handling conflicts from multiple knowledge sources. It demonstrates competitive performance across benchmarks, with significant gains in settings where direct inference or retrieval is weak.

The paper introduces REALM, a framework for long-term memory in LLM agents that uses retrieval-driven reconsolidation to autonomously organize memories into a cognitive graph, achieving improved performance on long-term memory benchmarks.

CUDA-Harness is a framework that uses agentic techniques to generate and optimize CUDA kernels from natural language descriptions, addressing challenges in Text2CUDA by connecting high-level semantics with low-level implementation and verification.

FoldingAgent is an agentic framework that uses vision-language models and specialized tools to infer parametric origami folding programs from demonstration videos via sequential reasoning and physical verification.

The paper introduces Image Bundle Composition (IBC) to shift image retrieval from atomic matching to dynamic composition of cohesive image bundles, addressing limitations in traditional approaches. It proposes a benchmark dataset IBCBench and an agentic framework BundleWeaver that leverages LLMs and VLMs for relational composition.

MCite-RL is a citation-enhanced agentic reinforcement learning framework designed for reliable multimodal RAG, introducing iterative retrieval and reasoning for visual citations and a reward mechanism to jointly optimize answer accuracy and source traceability.

This paper identifies tool-mediated recovery as a failure mode in LLM unlearning and proposes Agentic Tool Unlearning (ATU) to reduce both parametric recall and tool-based recovery while preserving normal tool use.

The paper introduces a knowledge-guided agentic framework that identifies missing patient context in health queries and asks targeted follow-up questions to improve the accuracy and consistency of downstream language model responses.

The author shares their hands-on experience setting up the OC agentic framework, troubleshooting voice model and local LLM issues, and building a secure, sandboxed workspace for AI agents.

This paper proposes an agentic framework combining rules and LLMs for embedding and annotating document layouts in plant science, demonstrating scalable trait extraction with improved annotation coverage.

This paper presents SABLE, an open-source agentic framework that uses natural-language orchestration with LLMs to guide synthetically constrained multi-objective hit-to-lead optimization in drug discovery, integrating reaction-templated enumeration, property prediction, and Bayesian optimization.

SCOUT introduces a recovery-aware agentic framework with an adaptive exploration-exploitation policy for ultra-long egocentric video reasoning, trained via UPS-GRPO, a uncertainty-prioritized RL method with turn-level advantage decomposition. It achieves state-of-the-art results on ultra-long egocentric benchmarks and remains competitive on shorter long-video settings.

CASCADE is an agentic framework that predicts downstream transcriptional effects of gene perturbations using regulatory networks, with patient-data validation across TCGA cancer types. The paper demonstrates directional concordance for MYC and other regulators, though it does not exceed existing curated knowledge.

VideoAgent is an all-in-one open-source framework for comprehensive video intelligence, combining understanding, editing, and creative generation through a unified agentic workflow.

EvoPINN is an agentic framework that reformulates PINN development as an execution-grounded algorithm discovery problem, using an LLM agent to propose programmatic modifications. It autonomously discovers PDE-specialized learning algorithms, including a novel architecture called SLRC-PINN, which outperforms baselines across diverse PDE regimes.

GoGoTB is an agentic framework for end-to-end RTL verification that achieves specification-grounded coverage closure, reaching high coverage on 8 designs without human intervention.

ProcAgent is a fully on-device, agentic, vision-based procedural assistant that uses a propose-and-verify architecture for real-time adaptive guidance on an NVIDIA Jetson AGX Orin. It supports both reactive and proactive modes with human-in-the-loop confirmation, achieving responsive interaction and positive user study ratings.