Tag
The paper introduces a failure-aware adversarial retrieval-augmented framework using contextual bandits to improve robustness in natural language understanding, with significant improvements on benchmarks like SNLI, ANLI, and MultiNLI.
DART-SD proposes a topology-aware retrieval and tuning framework for self-distillation of LLM-based tool-calling agents, improving policy diversity by correcting only critical topological breakpoints while preserving valid reasoning.
This paper proposes a risk-controlled framework for using LLMs as judges in factual evaluation, calibrating uncertainty thresholds to maintain a user-specified error rate and routing to retrieval-augmented mode when needed, achieving higher coverage with provable reliability guarantees.
This paper introduces DA-RAC, a distance-aware calibration method for LLM judges to enhance trustworthiness in AI auditing by using similar labeled anchors to reduce miscalibration and false-pass risks.
This paper introduces RAEF, a model-agnostic retrieval-augmented method for time series forecasting that improves accuracy and reduces computational overhead compared to fine-tuning approaches.
This paper presents a retrieval-augmented translation system using BM25 and Gemini 2.5 Flash for low-resource North-Eastern Indian languages, submitted to the WMT26 shared task without model fine-tuning.
This paper proposes HybridRAG-BN, a retrieval-augmented framework for Bangla knowledge-base question answering that combines hybrid retrieval, Gemma-based generation, and LoRA fine-tuned verification, achieving first place with F1 scores of 0.71654 and 0.72912.
Introduces Retrieval-Corrected Conformal Prediction (RCCP), a retrieval-augmented calibration method for time series prediction intervals that selects similar past residuals and applies a scalar conformal correction to achieve target coverage with low overhead.
The paper presents LLM Agents Factory, a retrieval-based framework that constructs domain-specific LLM agents from a base of over 20K predefined agent profiles, offering a cost-efficient and controllable alternative to dynamic agent generation. Experiments show accuracy comparable to AutoGen with a 120B backbone at substantially lower inference cost.
Align-RAG introduces a training-free, closed-form alignment method for retrieval-augmented forecasting with frozen Time Series Foundation Models, outperforming learned fusion adapters on standard benchmarks without any learned parameters.
This paper introduces UniME-R1, an embedder-adviser framework for unified multimodal retrieval that generates Retrieval-Centric Chain-of-Thought (RC-CoT) conditioned on retrieval feedback, improving retrieval performance by learning from hard negatives.
This paper introduces distractor-aware truncation to separate context-length effects from signal loss in long-context LLM benchmarks, showing that naive truncation conflates the two and that preserving task-relevant content while removing distractors maintains or improves performance.
The paper introduces EMBL AI Librarian, a knowledge layer that lets life-science AI agents query Europe PMC using natural language and receive direct evidence. It improves retrieval and downstream task performance across multiple benchmarks, with code released publicly.
WikiLoop is a feedback-coupled framework that jointly learns to build and navigate an agent-native Wiki, improving answer correctness on multiple benchmarks through a shared policy with sufficiency-before-efficiency objectives.
UniMem proposes a self-routing framework that combines episodic and parametric memory for LLM agents, enabling adaptive memory management in boundary-agnostic task streams without task labels.
ARC improves retrieval efficiency and accuracy in long-context AI agents by separating append-only storage from bounded citation views, achieving near-perfect recall with lower latency and bandwidth.
This paper investigates whether context-injected fine-tuning improves small language models' ability to use retrieved law for legal question answering in Bangladesh. Using Qwen3.5 at 0.8B, 2B, and 4B scales, they find fine-tuning helps at smaller scales but not at 4B, and reduces language drift.
This paper proposes a Damage Cause Encoder for bridge diagnostic agents that uses knowledge triple extraction from manuals and retrieval-augmented fine-tuning with QLoRA, achieving high accuracy with lower memory usage.
A developer shares how grounding an agent to a knowledge base with retrieval discipline, rather than a better model, solved hallucinations in automated slide generation. The approach splits retrieval from writing and enforces source checking before rendering.
This paper presents a retrieval-augmented, multi-agent LLM framework with human-in-the-loop for detecting cutaneous immune-related adverse events from clinical notes, achieving higher accuracy, improved inter-rater agreement, and halved review time compared to manual review.