4arXiv cs.CL (Computation and Language)·
FinSAgent: Multi-agent RAG framework for evidence-grounded SEC filing question answering
Researchers introduce FinSAgent, a multi-agent retrieval-augmented generation framework designed to address 'prior-corpus misalignment' in financial QA over SEC filings. The system combines role-specialized agents anchored to 10-K item structure, database-aware query decomposition, and a learned feature-gated reranker that separates evidential validity from semantic similarity. Evaluated across five offline financial QA benchmarks and a 1,000-user online experiment, FinSAgent outperforms single-agent and multi-agent baselines on retrieval coverage and answer correctness. The work targets a narrow but practically important domain where standard RAG pipelines fail due to structural and terminological mismatch.
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UMG-RAG: Training-free hybrid retrieval with uncertainty-aware granularity fusion for long-document RAG
Researchers propose Uncertainty-aware Multi-Granularity RAG (UMG-RAG), a training-free hybrid retrieval framework that addresses the tension between large and fine-grained retrieval chunks in RAG pipelines. The system converts dense and sparse retriever scores across multiple chunk granularities into evidence distributions, estimates reliability via entropy, and fuses candidates using query-specific confidence signals. A variant called UMGP-RAG uses fine-grained hits to locate evidence while returning broader parent chunks for coherence. Experiments on QA benchmarks show improved generation quality with no changes to the underlying retriever or generator.
Cost-Pragmatic Retrieval Gating and Multi-Model Fusion for BioASQ 2026 Biomedical QA
A BioASQ Task 14B 2026 system paper describes two core design decisions: a cost-pragmatic re-retrieval policy using a BGE cross-encoder quality gate, and a decomposition of multi-model ensemble lift into selection and fusion components. The retrieval pipeline unions dense (BGE + BM25 + RRF) and agent-driven PubMed/Europe PMC/iCite pipelines, achieving R@200 = 99.3% on the BioASQ-13b archive. The team places first on the combined-exact aggregate on three of eight leaderboard tracks and first on Phase B b3 ideal. GPT-5.5 solo retains list-F1 lead over a synonym-union resolver due to precision trade-offs.
Question-type-aware LLM pipeline with multi-agent collaboration achieves top factoid performance on BioASQ 14b
Researchers present a question-type-specific LLM framework for the BioASQ 14b biomedical QA challenge, applying distinct inference strategies to yes/no, factoid, and list questions rather than a single unified approach. For list questions, a multi-agent architecture handles evidence extraction, candidate generation, verification, and aggregation collaboratively. The framework achieved first place in the factoid subtask of Batch 4 in the official BioASQ 14b evaluation, demonstrating competitive performance across multiple batches.
GRADRAG: Cross-component prompt adaptation for coordinated multi-agent RAG pipelines
GRADRAG is a new framework that treats a multi-agent RAG pipeline as a computational graph and propagates structured evaluator feedback upstream to iteratively update retriever, graph constructor, and answerer prompts. Unlike prior work that optimizes components in isolation, GRADRAG coordinates improvements across the full pipeline via a Prompt Optimizer and an early-stopping Evaluator. Evaluated on SQUALITY and QMSUM benchmarks under both flat chunk-based and graph-based retrieval paradigms, GRADRAG achieves a 12–15 percentage point net preference margin over one-step refinement baselines in LLM-judged pairwise comparisons.
Multi-agent semantic rewriting framework for privacy-preserving RAG
A new arXiv preprint proposes a three-agent framework for sanitizing retrieved content in RAG pipelines by performing privacy extraction, semantic analysis, and reconstruction as an offline preprocessing step. Evaluated on ChatDoctor and Wiki-PII datasets across six LLMs, the approach reduces targeted information exposure in LLaMA-3-8B from 144 baseline instances to 1, while maintaining contextual fidelity (BLEU-1 of 0.122 vs. SAGE's 0.117). The framework introduces no additional online inference latency since rewriting is done offline. Source code is publicly released.
SearchOS: Multi-agent framework for robust open-domain information-seeking with explicit shared state
SearchOS is a new multi-agent framework that addresses a core failure mode in web-search-augmented LLM agents: repetitive search loops caused by implicit, untracked task progress. The system externalizes agent state into structured components (Frontier Task, Evidence Graph, Coverage Map, Failure Memory) via a Search-Oriented Context Management (SOCM) layer, and adds pipeline-parallel scheduling and a middleware harness to intercept tool interactions and recover from stalls. Evaluated on WideSearch and GISA benchmarks, SearchOS outperforms all single- and multi-agent baselines across all reported metrics.
FASE: Fast Adaptive Semantic Entropy for uncertainty quantification in multi-agent code generation
Researchers introduce Fast Adaptive Semantic Entropy (FASE), a metric for approximating functional correctness in LLM-generated code using minimum spanning trees of structural and semantic dissimilarity graphs, replacing costly LLM-driven equivalence checks. Evaluated on HumanEval and BigCodeBench with Qwen3-Embedding-8B, FASE achieves a 25% improvement in Spearman correlation and 19% increase in ROCAUC over prior semantic entropy methods. Critically, it requires only ~0.3% of the runtime cost of traditional semantic entropy approaches, making it practical for real-world multi-agent workflows.
TradingAgents-astock: A-share multi-agent investment research framework with 7 AI analysts
A GitHub project adapts the TradingAgents framework for Chinese A-share markets, incorporating local data sources (Dragon-Tiger lists, hot money flows, lock-up expiry schedules) and a bull/bear debate structure among 7 specialized AI analyst agents. The project has accumulated 2,111 stars with 230 added today, indicating significant community traction. It represents a domain-specific instantiation of multi-agent financial reasoning adapted to a non-US regulatory and data environment.