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Research Editor selected

Pre-Registered Audit of 25 Cognitive-Architecture Modules in an LLM Agent System

The paper introduces a four-question static audit protocol with line-number evidence and call-chain tracing to test whether named cognitive mechanisms in LLM-based agent systems are actually implemented and used. In the audited system claiming 25 cognitive mechanisms, the computed value never reached any production output in 18 modules; the authors identify three idle-wiring patterns: connected-but-never-called, called-but-inert, and called-but-content-blind. Across 61 frozen criteria in 12 interventions, 53 were confirmed, five falsified, two partially adjudicated, and one adjudication-ambiguous. Two repair experiments raised planner task success from 0.028 to 1.000 and cut state-controller error by 52%.

Why it matters: It gives agent developers a reusable audit method and concrete evidence that module names can substantially overstate implemented computation.

arxiv4d agoScore 82

LLM agentscognitive architectureAI evaluation

Research Editor selected

SecPO: Adversarial Training and New Loss Cut Prompt Injection Attack Success Rates Dramatically

Researchers propose SecPO, a principled adversarial training pipeline for defending LLM agents against prompt injection attacks. They introduce a SecPO loss that approximates an idealized injection-invariant reference model, and combine it with efficient synthesis of strong injected inputs. Their SecPO + AT model reduces attack success rates by up to 14.5x compared to Meta-SecAlign, and further 2.4-4.6x over DPO loss, while maintaining utility across nine benchmarks.

Why it matters: This paper presents a concrete, state-of-the-art defense against a critical AI security threat, with significant measured improvements and released resources.

arxiv4d agoScore 85

AI securityprompt injectionadversarial training

Research

TradeWar benchmark evaluates LLM trade negotiations by execution outcomes

Researchers introduced TradeWar, a multi-country simulation benchmark that converts negotiation dialogue into tariff and trade-share policies, applies capacity and network constraints during settlement, and scores outcomes with an Economic Health Index. They also propose TACTIC, an agent that refines negotiation strategy across episodes using prior experience, opponent behavior, and settlement feedback without updating base model weights. In reported results, PromptLLM achieved a 75% agreement rate but a 57.1 final EHI, while TACTIC reached 68.8; across 20 sequential episodes and three seeds, TACTIC improved final EHI from 58.2 to 73.2 versus 65.2 for an ablation without cross-episode updates. TACTIC also showed smaller performance degradation under technology-control and energy-crisis shocks.

arxiv4d agoScore 78

LLM agentsnegotiation benchmarkTradeWar

Research

EvoTeam Evolves Agent Teams, Interaction Rules and Skills Together

EvoTeam is a proposed framework for LLM-based multi-agent systems that jointly evolves team organization, team-specific interaction rules and individual agent skills using execution experience. It maintains a library of reusable teams, selecting or creating teams while keeping individual skills reusable across them. The authors report experiments across four benchmarks and provide a code repository, though the abstract gives no quantitative results.

arxivOct 30, 2026Score 78

EvoTeamMulti-agent systemsLLM agents

Research

Negotiation-Aware Hybrid LLM Framework Tackles Conflicting Cloud Intents

The paper proposes a framework that refines natural-language cloud intents, dynamically detects conflicts, and structures the result into a provider-independent TOSCA template. Candidate service offers are then retrieved through deterministic matching grounded in provider catalogs. Conflicts found during interpretation and requirements that prove unsatisfiable during discovery are resolved through iterative natural-language negotiation with the user. On 70 diverse intents the framework improves recall and F1 for both conflict detection and service discovery over single-shot and enhanced-prompting baselines, and eliminates hallucinated offers.

arxivOct 30, 2026Score 72

LLM agentscloud service compositionintent understanding

Research

EpiAgent: Epistemic Self-Play for LLM Agents

A new arXiv paper introduces EpiAgent, a framework that gives LLM-based agents an explicit, learnable epistemic layer holding beliefs about other agents' private information. The agent's policy is split into two coupled stages, Epistemic Update and Action Planning, whose dependency is formulated as a bi-level optimization problem. The authors propose Epistemic Self-Play, an algorithm that alternately optimizes both stages via self-play using an intrinsic prediction reward measuring how well inferred beliefs predict later observations. The abstract reports that EpiAgent outperforms prompting-based and RL-based baselines in mixed and competitive scenarios, with analyses pointing to gains in belief inference accuracy and in action planning from learned beliefs.

arxivOct 30, 2026Score 68

LLM agentsepistemic reasoningself-play

Research Editor selected

ClinGeX: A Skill-Centred LLM Agent for Clinical Genomic Analysis

Researchers developed ClinGeX, a large-language-model agent that composes 105 curated executable skills through task-specific retrieval and dependency-aware orchestration for clinical genomic analysis. On 223 temporally held-out benchmark cases, ClinGeX reached 71.7% macro-averaged task-level concordance with expert reference classifications, compared with 45.7% for the strongest comparator. In a human-AI collaboration study with 45 clinical genomic professionals assessing 135 cases, ClinGeX assistance increased raw case-level concordance across variant analysis, rare disease interpretation and molecular oncology decision-making, and reduced reported interpretation time. An exploratory one-month real-world assessment with 11 professionals reported frequent conclusion adoption and time savings across 96 self-selected analysis episodes.

Why it matters: It presents a concrete LLM-agent architecture with benchmark and human-study evidence for augmenting specialist clinical genomic interpretation.

arxivOct 30, 2026Score 87

ClinGeXclinical genomic analysisLLM agents

Research

FoG: Training KG Agents to Retrieve the Evidence Behind Their Answers

FoG is a framework for training knowledge-graph agents to retrieve the supporting triples behind factual answers, not merely produce correct answers. It combines evidence-grounded synthesis that hides explicit entities behind verifiable sub-questions, a KG interaction agent that records returned triples, and an evidence-coverage process reward that credits each supporting triple to the turn that first retrieves it. Experiments on three KGQA benchmarks show 3B–4B open-source agents outperforming substantially larger baselines in answer accuracy and evidence coverage, with zero-shot cross-KG transfer.

arxivOct 29, 2026Score 74

knowledge graphsLLM agentsevidence retrieval

Research

Concordat Enforces Typed Session Protocols on LLM Agents at Runtime

Concordat is an open-source Python library that treats an LLM agent as a finite-state protocol in the spirit of typestate and session types, and enforces that protocol at runtime. Each turn offers only tools legal in the current state, rejects out-of-protocol calls before side effects, allows human decisions only as requests, and charges token and tool-call budgets that split linearly across handoffs. Protocols are checked when defined, and a subclass is rejected if it could bypass its parent protocol. On tau-bench airline tasks with simulated customers pressuring agents to skip confirmation, a standard agent made changes without the required yes in 13 of 50 tasks with gpt-4.1 and 29 of 128 task-runs with Claude Haiku 5.5, while the Concordat agent made none, with no significant difference in task success; tau-bench’s reward missed most violations.

arxivOct 29, 2026Score 78

ConcordatLLM agentssession types