This paper asks what follows from a gap in answerability when discrepancies can affect what a target- and task-specific arrangement produces, accepts, or withdraws and which routes constrain the arrangement.
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.
CREST is proposed, an inference-time alignment method that steers base model hidden representations using safety directions extracted from a guidance model of any family, avoiding token-level structural limitations entirely and outperforming baselines by up to 22.2\% on safety benchmarks.
Reliable refusal behavior requires Large Language Models (LLMs) to reject harmful prompts with only answering benign ones. Incorrect refusal behavior can either expose users to harmful responses or prevent users from obtaining useful answers. Training-time alignment improves refusal behavior by updating model parameters with safety data, but requires additional computation and training. In contrast, inference-time alignment aims to modify LLM behavior during inference without updating the underlying model parameters. Existing inference-time methods mainly rely on in-context safety prompting, activation steering, or decoding control. However, most of them intervene without first determining whether the initial response is already appropriate, potentially altering a correct refusal or a useful answer. Effective selective intervention therefore requires identifying prompt intent beyond sensitive keywords, covering semantic variations that fixed rules may miss, and adapting the verifier to different base models. To address these challenges, we propose Response Inspection and Selective Actions (RISA), an inference-time framework that inspects the initial response and selectively corrects refusal errors without updating the base model. RISA first uses fixed contextual rules to assign refusal scores to clear cases. For unmatched cases, it derives a refusal score from the final-layer prompt hidden state using a calibrated linear probe. To adapt to different base models, RISA separately calibrates the probe score, representation-support boundary, and action thresholds. At runtime, RISA combines the prompt score with the initial refusal status and applies an action policy to intervene only when necessary. Experimental results demonstrate that RISA improves refusal reliability while largely preserving model utility, offering a practical solution for response-aware refusal calibration in LLMs.
Wenhan Chang, Tianqing Zhu, P. Xiong et al.· 0 citations
This work introduces MemeMind, which uses an offline reference answer to recover missing experience in Anime, Comic, and Game meme interpretation and shows that constructing successful tool use for failed groups provides the largest component gain and produces more effective evidence acquisition at inference time.
Run Yang, Weihang Wang, Boheng Sheng et al.· 1 citation
A new benchmark, KIFI, is designed, which comprises 1032 carefully selected instances from the TRUE and ScreenEval datasets, with key information annotated, and it is shown that LLMs frequently fail to use the appropriate information to make correct decisions.
Xindi Guo, Zhen Xie, Patrick H. Chen· Annual International ACM SIG...· 0 citations
This framework proposes a context-editing framework that performs selective abstraction over entities that appear in both the context and the question, establishing symbolic abstraction as a highly cost-efficient solution for ensuring context fidelity in LLMs.
Rounak Sharma, Debabrata Mahapatra, S. Saini· Annual International ACM SIG...· 0 citations
CROWN-QA is introduced, comprising CROWN-Synth, a controlled paired core that fixes the question and observed facts while varying only query-relative coverage, and CROWN-Real, a real-document contrast-set evaluation with controlled coverage variants.
Byoungjae Min, Kennedy Edemacu, Sae-Hong Cho et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
