Grounding Without Corrective Control: Truth-Tracking Profiles for Large Language Models
- URL: http://arxiv.org/abs/2608.14252v1
- Date: Fri, 14 Aug 2026 12:30:19 GMT
- Title: Grounding Without Corrective Control: Truth-Tracking Profiles for Large Language Models
- Abstract summary: Recent work suggests that some large language model representations have content or reference.<n>This paper asks what follows from that gap.<n>An output is answerable when discrepancies can affect what a target- and task-specific arrangement produces, accepts, or withdraws.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Recent work suggests that some large language model representations have content or reference. Grounding can secure either without supplying live routes for correction. This paper asks what follows from that gap. An output is answerable when discrepancies can affect what a target- and task-specific arrangement produces, accepts, or withdraws. The arrangement has corrective control only when live, sufficiently independent routes can detect and repair fresh discrepancies. A route profile records which routes constrain the arrangement and how they are related. Those profiles support analysis of truth-tracking: patterned support for representational success. Language models are the pressure case; text-only arrangements provide a task-relative limiting case. Text-trained models inherit patterns of testimony, coherence, and prior correction. Where target-sensitive correction survives training, these can supply derivative answerability (inherited constraint); live answerability is the relation supplied by a current route for fresh discrepancies. Fluent failures should follow when a task requires independently informative access to the facts. Self-consistency, retrieval, tools, code execution, multimodal input, and feedback should help selectively. Route-by-task interactions test the distinctions. The decomposition's empirical burden is to predict held-out route--task combinations or improve intervention choice without conceptual refitting. Surface improvement and truth-tracking improvement can come apart.
Related papers
- The Tell-Tale Trace: Detecting Reasoning Failures in LLMs Using Chain-of-Thought Dynamics [0.9668407688201359]
Chain-of-thought (CoT) reasoning improves large language model (LLM) performance.<n>Existing approaches that leverage verbalized CoTs to monitor reasoning correctness largely evaluate the semantic correctness or consistency of individual intermediate steps.<n>We show that capability failures can manifest as distributed, task-dependent changes in the structure of visible reasoning.
arXiv Detail & Related papers (2026-08-04T08:04:36Z) - H$^2$SD: Hybrid Hindsight Self-Distillation [61.71131241473028]
Reinforcement learning with verifiable rewards (RLVR) provides reliable outcome supervision for language model reasoning.<n>Existing self-distillation methods add a privileged teacher but typically assign it a fixed role.<n>We introduce Hybrid Hindsight Self-Distillation ($mathrmH2mathrmSD$), which jointly adapts teacher context and update strategy to trajectory correctness.
arXiv Detail & Related papers (2026-07-21T10:47:27Z) - When Correct Demonstrations Hurt: Rethinking the Role of Exemplars in In-Context Learning [14.102553871638243]
In-context learning (ICL) is often motivated by the intuition that demonstrations help because they provide correct input-output examples.<n>We show that correctness does not guarantee exemplar utility, and some correct demonstrations can even reduce ICL accuracy.<n>Our results show that robust ICL requires evaluating not only whether demonstrations are correct, but also how they influence contextual inference.
arXiv Detail & Related papers (2026-05-25T21:52:49Z) - Faithfulness as Information Flow: Evaluating and Training Faithful Chain-of-Thought Reasoning [10.87972575497941]
Chain-of-thought (CoT) reasoning is useful for monitoring language models.<n>Models can rely on prompt-to-answer shortcuts that bypass the CoT.<n>We study CoT faithfulness through a structural information-flow perspective.
arXiv Detail & Related papers (2026-05-22T23:37:29Z) - Affordance Agent Harness: Verification-Gated Skill Orchestration [45.231685718099264]
Affordance grounding requires identifying where and how an agent should interact in open-world scenes.<n>We propose Affordance Agent Harness, a closed-loop runtime that unifies skills with an evidence store and cost control.
arXiv Detail & Related papers (2026-05-01T13:45:16Z) - Learn to Rank: Visual Attribution by Learning Importance Ranking [58.69028273772474]
We propose a new approach to generate visual attribution maps for computer vision models.<n>Our method produces dense, pixel-level attributions in a single forward pass with optional, few-step gradient refinement.<n>Our experiments demonstrate consistent quantitative improvements and sharper, boundary-aligned explanations.
arXiv Detail & Related papers (2026-04-07T12:53:22Z) - Refinement Provenance Inference: Detecting LLM-Refined Training Prompts from Model Behavior [58.751981587234916]
This paper formalizes the Refinement Provenance Inference (RPI) audit task as Refinement Provenance Inference (RPI)<n>We propose RePro, a logit-based framework that fuses teacher-forced likelihood features with logit-ranking signals.<n>During training, RePro learns a transferable representation via shadow fine-tuning, and uses a lightweight linear head to infer provenance on unseen victims without training-data access.
arXiv Detail & Related papers (2026-01-05T10:16:41Z) - Mitigating Spurious Correlations Between Question and Answer via Chain-of-Thought Correctness Perception Distillation [25.195244084313114]
Chain-of-Thought Correctness Perception Distillation (CoPeD) aims to improve the reasoning quality of the student model.<n>CoPeD encourages the student model to predict answers based on correct rationales and revise them when they are incorrect.
arXiv Detail & Related papers (2025-09-06T05:33:17Z) - Beyond Semantics: The Unreasonable Effectiveness of Reasonless Intermediate Tokens [14.78605805191225]
We investigate how the semantics of intermediate tokens-often anthropomorphized as "thoughts" or reasoning traces-actually influence model performance.<n>We show that despite significant improvements on the solution-only baseline, models trained on entirely correct traces still produce invalid reasoning traces when arriving at correct solutions.
arXiv Detail & Related papers (2025-05-19T23:29:23Z) - CAFA: Class-Aware Feature Alignment for Test-Time Adaptation [50.26963784271912]
Test-time adaptation (TTA) aims to address this challenge by adapting a model to unlabeled data at test time.
We propose a simple yet effective feature alignment loss, termed as Class-Aware Feature Alignment (CAFA), which simultaneously encourages a model to learn target representations in a class-discriminative manner.
arXiv Detail & Related papers (2022-06-01T03:02:07Z) - PROMPT WAYWARDNESS: The Curious Case of Discretized Interpretation of
Continuous Prompts [99.03864962014431]
Fine-tuning continuous prompts for target tasks has emerged as a compact alternative to full model fine-tuning.
In practice, we observe a "wayward" behavior between the task solved by continuous prompts and their nearest neighbor.
arXiv Detail & Related papers (2021-12-15T18:55:05Z) - Robust Question Answering Through Sub-part Alignment [53.94003466761305]
We model question answering as an alignment problem.
We train our model on SQuAD v1.1 and test it on several adversarial and out-of-domain datasets.
arXiv Detail & Related papers (2020-04-30T09:10:57Z)
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.