Game-Theoretic Multi-Agent Control for Robust Contextual Reasoning in LLMs

• URL: http://arxiv.org/abs/2606.10322v2
• Date: Fri, 12 Jun 2026 14:11:13 GMT
• Title: Game-Theoretic Multi-Agent Control for Robust Contextual Reasoning in LLMs
• Abstract summary: Large Language Models (LLMs) in multi-turn interactions maintain evolving context rather than generating isolated responses.<n>We introduce the Game-Theoretic Secure Model Context Protocol (GT-MCP), a controller-driven multi-agent method that treats context management as a closed-loop dynamical process.<n>When instability is detected, a rollback-based self-healing mechanism restores the validated context and prevents unsupported fragments from propagating.
• Score: 6.335875360476155
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) in multi-turn interactions maintain evolving context rather than generating isolated responses, making them vulnerable to prompt-injection and context-poisoning attacks in which locally plausible adversarial fragments gradually distort reasoning trajectories. Existing defenses mainly filter individual outputs and often ignore context evolution across turns, leaving long-horizon reasoning exposed. Although the Model Context Protocol (MCP) standardizes context exchange and tool invocation, it functions as a passive routing layer and does not enforce contextual stability. To address these limitations, we introduce the Game-Theoretic Secure Model Context Protocol (GT-MCP), a controller-driven multi-agent method that treats context management as a closed-loop dynamical process. GT-MCP coordinates three heterogeneous LLM agents and selects outputs through a trust function that jointly evaluates causal consistency against a validated context graph, semantic agreement among agents, and distributional drift over time. When instability is detected, a rollback-based self-healing mechanism restores the validated context and prevents unsupported fragments from propagating. Empirical evaluation over 500 interaction turns under an adaptive adversarial threat model shows that contextual drift remains bounded in 99.6% of turns, with recovery required in only 0.4%. Per-turn utility remains tightly concentrated, with median = -0.19, P05 = -0.72, and P95 = 0.30; severe degradation below -1 occurs in only 0.4% of cases, and no injection attempt succeeds at the controller level. Selected outputs maintain stable win rates above 98%, and computational overhead remains predictable, with latency per token = 1.63e-3 s.

Related papers

• AutoCRAT: Within-trajectory Joint Control of Stochasticity and Compute for LLM Reasoning [10.408251801653723]
  Large language models (LLMs) achieve strong reasoning performance, which depends critically on time decisions.<n>Recent adaptive methods partially address this limitation, but they primarily adapt either recommended decodingity (how the model explores) or reasoning compute (how long the model reasons) in isolation.<n>We introduce a within-trajectory joint control view, and instantiate it in AutoCRAT.
  arXiv  Detail & Related papers  (2026-08-30T19:24:58Z)
• ContextShift: A Controlled Benchmark for Context Dependence in Object Detection [1.8320252176005443]
  We introduce ContextShift, a benchmark that manipulates object--context relationships while preserving object appearance.<n>We observe a consistent degradation pattern: false negatives increase by up to 227% and prediction volume decreases by up to 44%.<n>We show that context-aware augmentation improves robustness: every augmented variant outperforms the dataset-only baseline on both original and manipulated test images.
  arXiv  Detail & Related papers  (2026-06-08T13:48:16Z)
• Beyond the Attention Stability Boundary: Agentic Self-Synthesizing Reasoning Protocols [6.357772907811544]
  Self- Synthesizing Reasoning Protocols (SSRP) is a metacognitive framework that implements a discrete separation between architectural planning and procedural execution.<n>We present 3 experimental tiers: a shallow recency-based retrieval pilot, a high-entropy SOP, and a semantic hijacked 3-hop Multi-Fact Synthesis task.<n>Our results empirically locate the Attention Stability Boundary, where stateless Vanilla ReAct baselines for GPT 5.4 collapse to 0.1% success while SSRP achieves a 715X Resilience Lift.
  arXiv  Detail & Related papers  (2026-04-27T14:13:30Z)
• Cognitive Friction: A Decision-Theoretic Framework for Bounded Deliberation in Tool-Using Agents [0.0]
  Unconstrained tool-using agents must decide which information source to query and act on.<n>We propose the Triadic Cognitive Architecture (TCA), a decision-theoretic framework that formalizes these failure modes via cognitive friction.<n>We validate TCA in two controlled environments designed to isolate stopping quality, action selection under congestion, and temporal urgency.
  arXiv  Detail & Related papers  (2026-03-31T17:30:25Z)
• Dynamic analysis enhances issue resolution [53.50448142467294]
  DAIRA (Dynamic Analysis-enhanced Issue Resolution Agent) is an automated repair framework that embeds dynamic analysis into the agent's reasoning cycle.<n> Driven by a Test Tracing-Driven methodology, DAIRA utilizes lightweight monitors to extract critical runtime data.<n>When powered by Gemini 3 Flash Preview, DAIRA establishes a new state-of-the-art (SOTA) performance, achieving a 79.4% resolution rate on the SWE-bench Verified dataset.
  arXiv  Detail & Related papers  (2026-03-23T14:48:54Z)
• Improving Search Agent with One Line of Code [68.58667107354253]
  Tool-based Agentic Reinforcement Learning (TARL) has emerged as a promising paradigm for training search agents to interact with external tools.<n>We propose textbfSearch textbfAgent textbfPolicy textbfOptimization (textbfSAPO), which stabilizes training via a conditional token-level KL constraint.
  arXiv  Detail & Related papers  (2026-03-10T04:07:39Z)
• Adversarial Intent is a Latent Variable: Stateful Trust Inference for Securing Multimodal Agentic RAG [5.4716896485317195]
  Current stateless defences for multimodal agentic RAG fail to detect adversarial strategies that distribute malicious semantics.<n>We introduce MMA-RAGT, an inference-time control framework governed by a Modular Trust Agent (MTA) that maintains an approximate belief state.
  arXiv  Detail & Related papers  (2026-02-24T23:52:27Z)
• Information Fidelity in Tool-Using LLM Agents: A Martingale Analysis of the Model Context Protocol [69.11739400975445]
  We introduce the first theoretical framework for analyzing error accumulation in Model Context Protocol (MCP) agents.<n>We show that cumulative distortion exhibits linear growth and high-probability deviations bounded by $O(sqrtT)$.<n>Key findings include: semantic weighting reduces distortion by 80%, and periodic re-grounding approximately every 9 steps suffices for error control.
  arXiv  Detail & Related papers  (2026-02-10T21:08:53Z)
• Internalizing LLM Reasoning via Discovery and Replay of Latent Actions [4.830503861275364]
  Internalization of chain-of-thought processes into hidden states has emerged as a highly efficient paradigm for scaling test-time compute.<n>We propose STIR (Self-Distilled Tools for Internal Reasoning), a framework that reformulates reasoning enhancement as a dynamic latent trajectory control problem.
  arXiv  Detail & Related papers  (2026-02-04T08:44:57Z)
• VIRO: Robust and Efficient Neuro-Symbolic Reasoning with Verification for Referring Expression Comprehension [51.76841625486355]
  Referring Expression (REC) aims to localize the image region corresponding to a natural-language query.<n>Recent neuro-symbolic REC approaches leverage large language models (LLMs) and vision-language models (VLMs) to perform compositional reasoning.<n>We introduce VIRO, a neuro-symbolic framework that embeds lightweight operator-level verifiers within reasoning steps.
  arXiv  Detail & Related papers  (2026-01-19T07:21:19Z)
• Drift No More? Context Equilibria in Multi-Turn LLM Interactions [58.69551510148673]
  contexts drift is the gradual divergence of a model's outputs from goal-consistent behavior across turns.<n>Unlike single-turn errors, drift unfolds temporally and is poorly captured by static evaluation metrics.<n>We show that multi-turn drift can be understood as a controllable equilibrium phenomenon rather than as inevitable decay.
  arXiv  Detail & Related papers  (2025-10-09T04:48:49Z)
• Alignment Tipping Process: How Self-Evolution Pushes LLM Agents Off the Rails [103.05296856071931]
  We identify the Alignment Tipping Process (ATP), a critical post-deployment risk unique to self-evolving Large Language Model (LLM) agents.<n>ATP arises when continual interaction drives agents to abandon alignment constraints established during training in favor of reinforced, self-interested strategies.<n>Our experiments show that alignment benefits erode rapidly under self-evolution, with initially aligned models converging toward unaligned states.
  arXiv  Detail & Related papers  (2025-10-06T14:48:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.