training-free

This paper introduces Event Signature Transfer (EST), a training-free, model-agnostic operator that constructs forecast scenarios by transferring event signatures from historical events onto time-series forecasts.

This paper introduces an LLM-driven, training-free closed-loop paradigm for fusing dual-source encrypted POIs, improving location alignment and attribute matching for land-use/land-cover mapping, outperforming existing methods.

The paper presents a mechanistic analysis of over-refusal in large language models and proposes Semantic Routing Calibration (SRC), a lightweight, training-free inference framework to dynamically suppress hypersensitive safety heads and mitigate over-refusal while preserving intrinsic safety.

PRQuant is a training-free and low-overhead framework for quantizing linear layers in large language models, using permutation and residual compensation to reduce inference latency while improving accuracy over baselines like MXFP4.

A systematic study benchmarking training-free uncertainty quantification strategies for multimodal Large Language Models, categorizing methods into token-level, verbalized, and semantic approaches and finding optimal strategies depend on response length.

The paper introduces GeoPair, a training-free framework for transformer compression that optimizes cross-layer factorizations while preserving activation geometries, achieving state-of-the-art results across diverse architectures.

The paper introduces Neural Spectral Capacity (NSC), a training-free metric based on the singular-value spectrum to evaluate and optimize neural network architectures, with a dynamic programming method for optimal design under constraints.

The paper introduces Lens, a training-free framework for multimodal representation learning that addresses semantic perspective misalignment, achieving significant performance improvements on MMEB datasets without parameter updates.

This paper introduces EvoSkill-GUI, a training-free framework that allows GUI agents to improve skills through in-execution reflection, revision, and reuse, demonstrating performance gains on multiple benchmarks without retraining.

RefineEdit is a training-free prompt-to-prompt image editing method that uses a generative refinement network to enhance edit localization and background preservation, achieving top benchmark scores.

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.

This paper introduces the MUSE task to evaluate context updating in LLMs and proposes PLUME, a training-free method that improves performance in sequential evolution settings with significant gains on the MUSE-Bench benchmark.

The paper introduces a training-free, deterministic pipeline for lexical prompt compression in large language models, featuring empirical Pareto analysis across eleven task categories.

Decision-Flow Sampling is a training-free framework that extracts high-quality reasoning paths in large language models by constructing a hierarchical tree and performing global trajectory evaluation, outperforming existing sampling methods on benchmarks.

CoG is a cognitive-inspired, training-free framework for adaptive knowledge exploration in retrieval-augmented generation, achieving state-of-the-art performance on multi-hop QA benchmarks through plan-explore-reflect cycles and bidirectional graph-text synergy.

HarnessVLN is a zero-shot, training-free framework for embodied navigation that unifies perception, retrieval, grounding, navigation, recovery, and termination through a unified tool interface, achieving state-of-the-art results on benchmarks like R2R and RxR.

RSIAgent is a training-free multi-agent framework that enables digital agents to adapt to new environments through recursive self-improvement, autonomous memory construction, and broad-then-deep exploration, outperforming closed-source models on benchmarks.

ConvMem is a training-free, parallelizable framework that reformulates long-context reasoning in large language models as hierarchical convolution to improve efficiency, avoid overfitting, and outperform baseline methods.

The paper proposes a training-free and alignment-free approach to corporate intelligence using deterministic sparse seed vectors for analyzing SEC filings, enabling efficient document comparison and semantic event detection without large language models.

The paper presents World in World, a training-free interface that enables flexible camera and time control in frozen autoregressive video world models by using correspondence-guided queries and evidence-wise attention guidance.