Semantic Cooperative Games for Contribution Attribution in LLM-Based Multi-Agent Systems

• URL: http://arxiv.org/abs/2607.18255v1
• Date: Thu, 14 May 2026 03:18:52 GMT
• Title: Semantic Cooperative Games for Contribution Attribution in LLM-Based Multi-Agent Systems
• Abstract summary: We propose a framework that represents a realized language flow as a semantic generation hypergraph.<n>We define the Semantic Shapley Value (SSV) to allocate contribution over semantic support logic.<n>We prove that SSV reduces to the classical Shapley value under standard set-based, fully observable, and no-order-dependence conditions.
• Score: 17.736859646169773
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Contribution attribution has become a central problem in LLM-based multi-agent systems, where final outputs are produced through multiple agents, message exchanges, and ordered workflow dependencies. Existing attribution methods often rely on counterfactual valuation, such as removing agents or comparing score changes across altered agent subsets. In language-mediated workflows, these methods require repeated model calls, introduce high variance, and do not explicitly capture the intermediate semantic states through which agents produce, preserve, and transform task-relevant information. We propose Semantic Cooperative Games (SCG), a framework that represents a realized language flow as a semantic generation hypergraph and induces an agent-level semantic value function on this structure. We define the Semantic Shapley Value (SSV) to allocate contribution over semantic support logic, and introduce SLIC, a single-trajectory algorithm that constructs the semantic hypergraph, recovers minimal semantic supports, applies Boolean absorption, and computes SSV without rerunning agent subsets. We prove that SSV reduces to the classical Shapley value under standard set-based, fully observable, and no-order-dependence conditions. On a medical benchmark satisfying these conditions, SLIC reduces computation cost by 93.3% while remaining highly consistent with a Monte Carlo Shapley baseline. In more general multi-role workflows, SSV aligns with perturbation-induced score-drop profiles and exposes cases where semantic contribution and failure impact diverge. Overall, SLIC provides a fast, counterfactual-free, and interpretable attribution method for complex LLM-based multi-agent systems.

Related papers

• Graph-based Target Back-Propagation for Context Adaptation in Multi-LLM Agentic Systems [1.6138730252470326]
  Context adaptation automates prompt engineering in LLM-based systems by iteratively revising prompts from task feedback, without modifying model weights.<n>We propose textbfGraph-based textbfTarget textbfBack-textPropagation (GTBP), a context adaptation framework for agentic graphs modeled as directed acyclic graphs.
  arXiv  Detail & Related papers  (2026-06-12T06:27:15Z)
• Role-Agent: Bootstrapping LLM Agents via Dual-Role Evolution [25.26084224278813]
  Role-Agent harnesses a single Large Language Model (LLM) to function concurrently as both the agent and the environment.<n>Role-Agent consistently improves performance, yielding an average gain of over 4% over strong baselines.
  arXiv  Detail & Related papers  (2026-06-09T14:28:07Z)
• Context Pruning for Coding Agents via Multi-Rubric Latent Reasoning [14.508049757681862]
  LLM-powered coding agents spend the majority of their token budget reading repository files.<n>We show that this formulation creates a modeling bottleneck.<n>We propose LaMR, a structured pruning framework that decomposes code relevance into two interpretable quality dimensions.
  arXiv  Detail & Related papers  (2026-05-14T18:30:10Z)
• Job Skill Extraction via LLM-Centric Multi-Module Framework [51.37063712967151]
  We present SRICL, an LLM-centric framework that combines semantic retrieval, in-context learning, and supervised fine-tuning.<n>On six public span-labeled corpora of job-ad sentences, SRICL achieves substantial STRICT-F1 improvements over GPT-3.5 prompting baselines and sharply reduces invalid tags and hallucinated spans.
  arXiv  Detail & Related papers  (2026-04-23T10:46:07Z)
• PCA-Seg: Revisiting Cost Aggregation for Open-Vocabulary Semantic and Part Segmentation [58.1914505657064]
  This paper proposes a simple yet effective parallel cost aggregation (PCA-Seg) paradigm to alleviate the challenge of knowledge interference between class-level semantics and spatial context.<n>Experiments on eight benchmarks show that each parallel block in PCA-Seg adds merely 0.35M parameters while achieving state-of-the-art OSPS performance.
  arXiv  Detail & Related papers  (2026-03-18T09:26:43Z)
• Beyond Linear LLM Invocation: An Efficient and Effective Semantic Filter Paradigm [17.52767415071768]
  Clustering-Sampling-Voting (CSV) is a framework that reduces invocations to sublinear complexity while providing error guarantees.<n>CSV embeds semantic clusters into semantic clusters, samples a small subset for evaluation, and infers cluster-level labels via two proposed voting strategies.
  arXiv  Detail & Related papers  (2026-03-05T04:37:15Z)
• Refer-Agent: A Collaborative Multi-Agent System with Reasoning and Reflection for Referring Video Object Segmentation [50.22481337087162]
  Referring Video Object (RVOS) aims to segment objects in videos based on textual queries.<n>Refer-Agent is a collaborative multi-agent system with alternating reasoning-reflection mechanisms.
  arXiv  Detail & Related papers  (2026-02-03T14:48:12Z)
• PEFA-AI: Advancing Open-source LLMs for RTL generation using Progressive Error Feedback Agentic-AI [5.455262834289454]
  We present an agentic flow consisting of multiple agents that collaboratively complete the task of Register Transfer Level (RTL) generation without human intervention.<n>A key feature of the proposed flow is the progressive error feedback system of agents (PEFA), a self-correcting mechanism.<n>To validate this adaptive approach to code generation, benchmarking is performed using two opensource natural language-to-RTL datasets.
  arXiv  Detail & Related papers  (2025-11-06T00:19:47Z)
• Language Models as Semantic Augmenters for Sequential Recommenders [20.934150208996048]
  Large Language Models (LLMs) excel at capturing latent semantics and contextual relationships across diverse modalities.<n>We introduce LaMAR, a framework designed to enrich such sequences automatically.<n>LaMAR generates auxiliary contextual signals by inferring latent semantic aspects of a user's intent and item relationships from metadata.
  arXiv  Detail & Related papers  (2025-10-20T19:36:38Z)
• Discrete Tokenization for Multimodal LLMs: A Comprehensive Survey [69.45421620616486]
  This work presents the first structured taxonomy and analysis of discrete tokenization methods designed for large language models (LLMs)<n>We categorize 8 representative VQ variants that span classical and modern paradigms and analyze their algorithmic principles, training dynamics, and integration challenges with LLM pipelines.<n>We identify key challenges including codebook collapse, unstable gradient estimation, and modality-specific encoding constraints.
  arXiv  Detail & Related papers  (2025-07-21T10:52:14Z)
• IDA-Bench: Evaluating LLMs on Interactive Guided Data Analysis [60.32962597618861]
  IDA-Bench is a novel benchmark evaluating large language models in multi-round interactive scenarios.<n>Agent performance is judged by comparing its final numerical output to the human-derived baseline.<n>Even state-of-the-art coding agents (like Claude-3.7-thinking) succeed on 50% of the tasks, highlighting limitations not evident in single-turn tests.
  arXiv  Detail & Related papers  (2025-05-23T09:37:52Z)
• Amortizing intractable inference in large language models [56.92471123778389]
  We use amortized Bayesian inference to sample from intractable posterior distributions. We empirically demonstrate that this distribution-matching paradigm of LLM fine-tuning can serve as an effective alternative to maximum-likelihood training. As an important application, we interpret chain-of-thought reasoning as a latent variable modeling problem.
  arXiv  Detail & Related papers  (2023-10-06T16:36:08Z)

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.