SEVA: Self-Evolving Verification Agent with Process Reward for Fact Attribution

• URL: http://arxiv.org/abs/2606.29713v1
• Date: Mon, 29 Jun 2026 02:37:13 GMT
• Title: SEVA: Self-Evolving Verification Agent with Process Reward for Fact Attribution
• Abstract summary: SEVA is a structured verification agent that emits evidence alignments, step-by-step reasoning chains, calibrated confidence, and a six-category error diagnosis.<n>On ClearFacts, SEVA-3B matches GPT-4o-mini (69.0 vs. 69.8 F1) while producing substantially richer, auditable output.
• Score: 6.908637308550535
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Hallucination is the reliability bottleneck for LLM-based agents, and fact attribution verifiers are the last line of defense -- yet today's verifiers emit only opaque binary labels, leaving agents unable to self-correct and operators unable to audit. We present SEVA, a structured verification agent that emits evidence alignments, step-by-step reasoning chains, calibrated confidence, and a six-category error diagnosis with actionable fixes. Training such an agent with RL is non-trivial: standard binary reward on multi-component output triggers advantage collapse -- within-group reward variance vanishes and the GRPO gradient disappears. We resolve this with a process reward that decomposes verification quality into five independent components weighted 70/30 toward process signals, restoring the gradient and inducing an implicit curriculum -- the agent first masters verification behavior (alignment 0.917 -> 0.997, format 72% -> 100%), then outcomes (F1 64.9 -> 69.0). Structured output further enables a Verify -> Reflect -> Probe -> Refine self-evolution loop, which over four rounds on a 7B model surfaces an unexpected structural finding: each round produces a benchmark-specialist, not a generalist (+15 pp on HaluEval, -10 to -14 pp on TruthfulQA in the same model, persistent at 4x data). On ClearFacts, SEVA-3B matches GPT-4o-mini (69.0 vs. 69.8 F1) while producing substantially richer, auditable output -- confirming a principle that should generalize: for any RL task with multi-component generation, reward granularity must match output granularity.

Related papers

• Persistent Semantic Entities in Tool-Augmented LLM Systems [3.556355987197792]
  Tool-augmented LLM agents can harbor implicit state that persists across sessions, activates through events, and propagates across agent boundaries.<n>We formalize this as Persistent Semantic Entities (PSEs)<n>We evaluate them across 24 models from 11 families (1.5B--1T parameters)
  arXiv  Detail & Related papers  (2026-08-08T06:31:00Z)
• LLM-as-a-Verifier: A General-Purpose Verification Framework [74.40111651545979]
  We introduce LLM-as-a-Verifier, a general-purpose verification framework.<n>It provides fine-grained feedback for agentic tasks without requiring additional training.<n>It achieves state-of-the-art performance on several benchmarks.
  arXiv  Detail & Related papers  (2026-07-06T17:59:35Z)
• Closing the Reflection Gap: A Free Calibration Bonus for Agentic RL [12.234169944475537]
  RefGRPO is a simple yet effective fix that augments standard RL algorithms with two key ingredients.<n>A free calibration bonus computed by contrasting the agent's own reflection with the actual outcome.<n>The resulting reflection turns the agent into its own verifier grounded in environment feedback.
  arXiv  Detail & Related papers  (2026-06-12T07:47:15Z)
• Verifier-First Evaluation of Agentic LLMs for Infrastructure-as-Code Generation [0.0]
  Infrastructure-as-Code (IaC) generation from natural language requires satisfying provider schemas, dependency planning, and organizational policy constraints.<n>We present a verifier-first empirical study of seven agentic strategies for Terraform generation evaluated on IaC-Eval v2, a modernized 186-task AWS/Terraform benchmark with Rego v1 policies.
  arXiv  Detail & Related papers  (2026-05-29T12:43:27Z)
• Automating Formal Verification with Reinforcement Learning and Recursive Inference [0.0]
  We train open-source models in Dafny with verifiable rewards (RLVR) and verifier-guided inference-time search.<n>With a fixed base model, the full scaffold with proof reviser improves pass rate on an initial VeriCoding pilot set from 46.2% under direct repair to 69.2%.<n>We also introduce Dalek-Bench, a repository-scale Lean benchmark derived from the Rust $texttcurve25519-dalek$ verification project.
  arXiv  Detail & Related papers  (2026-05-29T06:59:28Z)
• Distributional Energy-Based Models for Uncertainty-Aware Structured LLM Reasoning [40.342912574072024]
  Large Language Models produce structured outputs such as travel plans or code solutions.<n>Individual reasoning steps may appear correct while the output as a whole violates budgets, fails test cases, or contradicts earlier deductions.<n>We propose a learned quality scorer with deterministic analytical constraint penalties for verifying structured LLM outputs.
  arXiv  Detail & Related papers  (2026-05-15T17:08:27Z)
• AgentV-RL: Scaling Reward Modeling with Agentic Verifier [63.55502685076245]
  Verifiers have been demonstrated to enhance LLM reasoning via test-time scaling (TTS)<n>We propose Agentic Verifier, a framework that transforms reward modeling into a multi-turn, tool-augmented deliberative process.<n>We show that Agentic Verifier yields consistent performance gains under both parallel and sequential TTS.
  arXiv  Detail & Related papers  (2026-04-17T12:27:36Z)
• AgentHER: Hindsight Experience Replay for LLM Agent Trajectory Relabeling [12.42698406379544]
  AgentHER recovers lost training signal by adapting the Hindsight Experience principle to natural-language agent trajectories.<n>It converts discarded failures into high-quality SFT, DPO, and ShareGPT training data.<n>On WebArena Replay (Zhou et al., 2024) and ToolBench (Qin et al., 2024), AgentHER improves over success-only SFT by +7.1-11.7 pp across four model families.
  arXiv  Detail & Related papers  (2026-03-22T18:36:58Z)
• CoVerRL: Breaking the Consensus Trap in Label-Free Reasoning via Generator-Verifier Co-Evolution [52.691495954442985]
  CoVerRL is a framework where a single model alternates between generator and verifier roles, with each capability bootstrapping the other.<n> Experiments across Qwen and Llama model families demonstrate that CoVerRL outperforms label-free baselines by 4.7-5.9% on mathematical reasoning benchmarks.<n>Self-verification accuracy improves from around 55% to over 85%, confirming that both capabilities genuinely co-evolve.
  arXiv  Detail & Related papers  (2026-03-18T14:38:55Z)
• PRIME: A Process-Outcome Alignment Benchmark for Verifiable Reasoning in Mathematics and Engineering [71.15346406323827]
  We introduce PRIME, a benchmark for evaluating verifiers on Process-Outcome Alignment verification.<n>We find that current verifiers frequently fail to detect derivation flaws.<n>We propose a process-aware RLVR training paradigm utilizing verifiers selected via PRIME.
  arXiv  Detail & Related papers  (2026-02-12T04:45:01Z)
• CVeDRL: An Efficient Code Verifier via Difficulty-aware Reinforcement Learning [57.24524263804788]
  Code verifiers play a critical role in post-verification for LLM-based code generation.<n>Existing supervised fine-tuning methods suffer from data scarcity, high failure rates, and poor inference efficiency.<n>We show that naive RL with only functionality rewards fails to generate effective unit tests for difficult branches and samples.
  arXiv  Detail & Related papers  (2026-01-30T10:33:29Z)
• Replayable Financial Agents: A Determinism-Faithfulness Assurance Harness for Tool-Using LLM Agents [0.7699235580548228]
  LLM agents struggle with regulatory audit replay: when asked to reproduce a transaction flagged decision with identical inputs, most deployments fail to return consistent results.<n>This paper introduces the DeterminismFaithfulness Assurance Harness (DFAH), a framework for measuring trajectory determinism and evidence-conditioned faithfulness in tool-using agents deployed in financial services.
  arXiv  Detail & Related papers  (2026-01-17T19:47:55Z)
• CARE What Fails: Contrastive Anchored-REflection for Verifiable Multimodal [84.71254539482369]
  Group-relative reinforcement learning with verifiable rewards (RLVR) often wastes the most informative data it already has the failures.<n>We present CARE, a failure-centric post-training framework for multimodal reasoning that turns errors into supervision.<n> CARE improves accuracy and training smoothness while explicitly increasing the share of learning signal that comes from failures.
  arXiv  Detail & Related papers  (2025-12-22T16:34:21Z)
• Eigen-1: Adaptive Multi-Agent Refinement with Monitor-Based RAG for Scientific Reasoning [53.45095336430027]
  We develop a unified framework that combines implicit retrieval and structured collaboration.<n>On Humanity's Last Exam (HLE) Bio/Chem Gold, our framework achieves 48.3% accuracy.<n>Results on SuperGPQA and TRQA confirm robustness across domains.
  arXiv  Detail & Related papers  (2025-09-25T14:05:55Z)

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.