Twin Worlds: Equivariance-Based Abstention for Evidence-Grounded Reasoning

• URL: http://arxiv.org/abs/2608.28018v1
• Date: Fri, 28 Aug 2026 07:33:29 GMT
• Title: Twin Worlds: Equivariance-Based Abstention for Evidence-Grounded Reasoning
• Abstract summary: We propose a framework for improving reliability in knowledge-intensive reasoning through equivariance-based abstention.<n>A key contributing factor is that entity mentions in context activate memorised associations, causing models to generate plausible responses ungrounded in evidence.<n>We construct multiple worlds via typed substitutions of the original input that preserve relational structure while reducing parametric priors.
• Score: 38.63002496590365
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Knowledge-intensive reasoning requires Large Language Models (LLMs) to ground answers in provided evidence. When evidence is insufficient, it is desirable that models abstain rather than confidently generating unsupported answers. Existing abstention methods rely on uncertainty estimation or evidence sufficiency checks, but neither tests whether the reasoning process for generation, driven by the interaction of provided evidence and the model's internal memory parameters, is actually grounded in the evidence. A key contributing factor is that entity mentions in context activate memorised associations, causing models to generate plausible responses ungrounded in evidence. We propose Twin Worlds (TW), a framework for improving reliability in knowledge-intensive reasoning through equivariance-based abstention: unlike invariance, which requires outputs to remain unchanged, equivariance requires outputs to transform correspondingly under entity substitutions. A model grounded in the evidence should produce answers that shift consistently when entities are substituted while their relations are preserved. TW constructs multiple worlds via typed substitutions of the original input that preserve relational structure while reducing parametric priors, and uses equivariance violations as an abstention signal. Across four benchmarks and three model backbones, TW identifies when answers are not reliably grounded in the provided evidence and outperforms uncertainty- and sufficiency-based baselines.

Related papers

• DERELAB: Probing Defeasible Reasoning and Confirmation Bias in LLMs with a Generative Benchmark [6.835919520017402]
  Defeasible reasoning is a type of reasoning where inferences are drawn from plausible current evidence, but can be retracted upon the introduction of newer evidence.<n>We introduce DeReLab, a generative framework that produces multi-turn belief-updating conversations from parameterized graph structures.<n>We evaluate nine open and proprietary large language models and find that nearly all exhibit a systematic tendency to accept congruent evidence while resisting incongruent updates.
  arXiv  Detail & Related papers  (2026-08-31T08:10:29Z)
• COSMO: Consensus-Driven Shift Modulation for Source-Free Domain Adaptation [79.43842208099619]
  Source-free domain adaptation (SFDA) adapts a source-trained model to an unlabeled target domain without source data.<n>We formulate VLM-guided SFDA as a sample-wise reliability-allocation problem.<n>We propose Consensus-Driven Shift Modulation (COSMO) to replace expert-to-expert guidance with co-adaptation.
  arXiv  Detail & Related papers  (2026-08-05T09:10:30Z)
• When benchmark inferences do not compose: Projectibility in AI evaluation [0.0]
  An AI benchmark result rarely reaches a consequential claim in one step.<n>Evaluators generalize it to further cases, interpret it as evidence of capability, extrapolate it to new tasks, transport it to another system or site.<n>This paper identifies a further problem: warranted links don't automatically make a warranted chain.
  arXiv  Detail & Related papers  (2026-07-28T18:07:04Z)
• HiEviDR-Bench: A Benchmark for Hierarchical Evidence Aggregation in Deep Research [52.87128503345243]
  We introduce HiEviDR-Bench, a benchmark for evaluating Hierarchical Evidence Aggregation in Deep Research.<n>HiEviDR-Bench represents each instance with an explicit evidence graph that captures evidence selection, cross-source linking, and aggregation.<n>We develop a traceability-oriented evaluation framework with five dimensions: report quality, evidence traceability, citation accuracy, claim verification, and answer correctness.
  arXiv  Detail & Related papers  (2026-07-27T23:50:46Z)
• Does Reasoning Preserve Alignment? On the Trustworthiness of Large Reasoning Models [55.788110316999166]
  We compare reasoning models produced via supervised fine-tuning, RL-based post-training, and distillation against instruction-tuned baselines.<n>We observe that reasoning models often improve on reasoning benchmarks but exhibit alignment regressions.<n>These regressions are consistent with behavioral drift from the instruction-tuned baseline, measured by KL divergence.
  arXiv  Detail & Related papers  (2026-06-09T16:14:27Z)
• The Digital Twin Counterfactual Framework: A Validation Architecture for Simulated Potential Outcomes [0.0]
  Digital Twin Counterfactual Framework (DTCF)<n>Five-level validation architecture converts unfalsifiable claim that simulator produces correct counterfactuals into falsifiable tests against observable data.<n> decomposition separates causal quantities into those that are marginally validated.<n>Joint causal claims become explicitly assumption-indexed.
  arXiv  Detail & Related papers  (2026-04-01T19:04:15Z)
• ERA: Evidence-based Reliability Alignment for Honest Retrieval-Augmented Generation [19.616905640490252]
  We propose a new framework called ERA (Evidence-based Reliability Alignment) to enhance abstention behavior in RAG systems.<n>Our approach significantly outperforms baselines, optimizing the trade-off between answer coverage and abstention with superior calibration.
  arXiv  Detail & Related papers  (2026-02-24T01:45:51Z)
• Retrieval-Infused Reasoning Sandbox: A Benchmark for Decoupling Retrieval and Reasoning Capabilities [32.76303717104482]
  We introduce DeR2, a controlled deep-research sandbox that isolates document-grounded reasoning.<n>DeR2 decouples evidence access from reasoning via four regimes--Instruction-only, Concepts, Related-only, and Full-set.<n> Experiments across a diverse set of state-of-the-art foundation models reveal substantial variation and significant headroom.
  arXiv  Detail & Related papers  (2026-01-29T16:26:19Z)
• CRAVE: A Conflicting Reasoning Approach for Explainable Claim Verification Using LLMs [15.170312674645535]
  CRAVE is a Conflicting Reasoning Approach for explainable claim VErification.<n>It can verify complex claims based on the conflicting rationales reasoned by large language models.<n>CRAVE achieves much better performance than state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-04-21T07:20:31Z)
• Nonparametric Identifiability of Causal Representations from Unknown Interventions [63.1354734978244]
  We study causal representation learning, the task of inferring latent causal variables and their causal relations from mixtures of the variables. Our goal is to identify both the ground truth latents and their causal graph up to a set of ambiguities which we show to be irresolvable from interventional data.
  arXiv  Detail & Related papers  (2023-06-01T10:51:58Z)
• Counterfactual Invariance to Spurious Correlations: Why and How to Pass Stress Tests [87.60900567941428]
  A spurious correlation' is the dependence of a model on some aspect of the input data that an analyst thinks shouldn't matter. In machine learning, these have a know-it-when-you-see-it character. We study stress testing using the tools of causal inference.
  arXiv  Detail & Related papers  (2021-05-31T14:39:38Z)

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.