inference-time

This paper shows that zeroing out 729 task-redundant attention heads in GPT-2-XL at inference time reduces verbatim memorisation by 91.5% while slightly improving WikiText-103 perplexity, offering a training-free way to mitigate privacy leakage from LLMs.

This paper identifies and formalizes 'inference-time model shift'—a change in conformal score distributions caused by varying inference configurations like quantization and temperature rather than data distribution changes—showing it breaks split-conformal exchangeability and overstates deployment-time coverage across 8 language models and 4 finite-label benchmarks.

提出 First-Token Cueing 行为探针，发现大模型许多闭卷事实问答错误源于无法生成正确的首 token，而仅提供该首 token 即可让模型完成并识别出正确答案，说明大量错误来自可恢复的首步解码失败而非知识缺失。

ConicFlow 将流匹配模型的约束采样建模为推理时的最优控制问题，采用滚动时域原对偶算法在满足硬终端约束的同时高奖励生成并少偏离预训练轨迹，理论上给出了次优性保证并在实验中优于现有基线。

REDaCT提出一种面向大型推理模型的推理时无遗忘框架，通过二元泄漏检测器与纠正性嵌入优化，在彻底移除遗忘集信息的同时完全避免推理链崩溃，实现近乎完美的答案与CoT遗忘并显著保留推理能力。

Nullify 提出一种免训练、非破坏性的激活引导方法用于 LLM 知识遗忘，在推理时通过满足零空间约束的引导向量重定向隐私相关激活，在 TOFU 和 MUSE 基准上匹配或超越现有基线，同时近乎无损地保留模型效用。

AAMAS 2027 论文提出将 LLM 智能体持久记忆的治理分为「准入」与「呈现」两个决策，并提出两种免重训练的推理时设计（FC 与 PS），在外部基准上分别降低 6.7 与 8.8 个百分点的失败率，同时显著减少跨领域信息泄露，但均加剧个性化失败，表明准入选择可带来任务特定的安全收益而有益记忆的保留仍未解决。

该论文提出两种闭式投影算子，可在推理时直接修正 AlphaFold3 风格扩散模型输出中的物理无效结构（链间碰撞、键长畸变、非平面环、手性翻转），无需重训练或额外网络评估，在 Boltz-2 与 OpenFold-3 上实现完美的物理有效性同时保持结构精度。

OmniConfess proposes a training-free inference-time method that re-scores fixed candidate responses at token resolution under channel-wise evidence interventions to expose and mitigate omni-modal hallucinations in OmniLLMs. The paper also introduces OmniHalluBench, a 3,540-example benchmark spanning text, image, audio, and video tasks.

Amazon and UCF researchers introduce SatisDive, a training-free inference-time method that formulates text-to-image generation as satisficing—requiring each candidate to meet a reward floor and the batch to meet a diversity cutoff—showing that varying the reward floor traces a Pareto frontier between worst-candidate reward and batch diversity, and that SatisDive Pareto-dominates FK steering on Pick-a-Pic with FLUX.1-dev and SANA-1.6B.

The paper shows that PRM-Pruned Fragment Grafting (PPFG), a cost-minimal inference-time cross-trajectory transfer technique, is statistically indistinguishable from independent parallel CoT across three reasoning LMs and six benchmarks, with the inertness traced to poor injection targeting and selection-dominated pruning dynamics.

Meta Superintelligence Labs introduces agentic meta-reasoning, an inference-time harness where a controller decides what work to run next in long agent runs, boosting ProgramBench from 63.7% to 71.5% with GPT-5.5 under the same workers and budget. Gains of 3.6-4.2 points hold across ProofBench, ARC-AGI-2 and LongCoT-mini when averaged over frontier models.

Amazon Science proposes Demographic Pluralism, an inference-time framework that estimates population-level opinion distributions by generating multiple perspectives within demographically grounded groups, reducing Jensen-Shannon distance by 8.4%-26.4% over Modular Pluralism across four backbones on GlobalOpinionQA and VITAL without requiring opinion-distribution training data.

论文提出 Temporal Activation Injection (TAI)，通过分析 VideoLLM 中帧序反转引发的层间表征差异（temporal divergence vector），在推理时沿实测衰减曲线重新注入时序信息，无需训练即可在三个 VideoLLM 和四个基准上一致提升时序推理能力。

The paper introduces Coffee, a plug-and-play framework that guides discrete diffusion models at inference time using compiled finite-state objectives, avoiding exponential enumeration of token completions while supporting hard constraints and learned soft objectives across symbolic, language, and biological benchmarks.

LocUS is a novel method for targeted activation steering in large language models that restricts interventions to specific attention heads and a subspace of the unembedding matrix, improving performance while preserving general capabilities.

The paper introduces target-speaker unlearning ASR (TSU-ASR) and proposes a novel Enrollment-Conditioned Gating module for dynamic opt-out of speakers during inference in LLM-based ASR, enhancing privacy in online conferencing.

This paper evaluates the capability and efficiency of large language models using Chain-of-Thought reasoning, finding that capability gains diminish with increasing model size, while efficiency shows little improvement.

The paper introduces MIRAGE, an inference-time framework for enhancing LLM reasoning by dynamically switching perspectives using reinforcement learning guidance, outperforming existing prompting methods on various benchmarks.

This paper presents AREA, a training-free inference-time method that adaptively allocates evidence highlighting in multimodal large language models, improving performance on knowledge-based visual question answering and standard multimodal benchmarks.