training-free

Introduces Archer, a training-free KV caching method for diffusion language models that adaptively reuses cached hidden states to reduce recomputation while preserving rollback capabilities, achieving up to 2.95x speedup and improved generation quality.

This paper introduces SkillAligner, a training-free framework that treats retrieved skills as adaptable drafts, jointly adapting them to task requirements, execution environments, and other skills to mitigate skill-execution misfit and improve agent performance.

KReF introduces a training-free retrieval framework for long-term time-series forecasting that constructs empirical predictive distributions from similar historical lookback-future pairs, achieving strong CRPS performance across multiple benchmarks.

KLQ is a training-free LLM quantization method that allocates bits per direction based on measured KL divergence, outperforming existing training-free rotation-based methods on W4A4KV4-bit settings for models like Llama 3.2 1B and Qwen 2.5.

The paper introduces Constraint-First Reasoning (CFR), a training-free two-stage prompting protocol that extracts answer-space constraints before solving and checks intermediate/final results against them, improving math problem solving on competition benchmarks.

ConWriter introduces a training-free framework for long-form story generation that maintains narrative consistency through scene-level incremental writing, symbolic state reasoning, and uncertainty-aware risk signals. Evaluated on ConStory-Bench across multiple models and lengths, it aims to prevent consistency errors from propagating in extended contexts.

This paper introduces elbow-based routing, a training-free inference-time method for MoE models that dynamically adjusts the number of active experts per token by detecting the elbow point in router probability distributions, achieving a 5.3% average latency reduction while maintaining accuracy.

This paper proposes a training-free, uncertainty-aware inference framework for using large language models in operations research. The method uses short lookahead simulations and importance resampling to improve the coherence of mathematical formulations, outperforming standard baselines on OR benchmarks.

OmniPack proposes a training-free token compression framework for omni-modal LLMs, combining structural pre-LLM compression with task-relevant inner-LLM semantic refinement, achieving strong performance-efficiency trade-offs on multiple benchmarks.

GaussianSelector is a training-free framework for interactive 3D object selection from sparse views using scribble guidance, operating directly on Gaussian primitives via graph-cut optimization.

Prox is a training-free framework for sparse SwiGLU FFNs in LLMs, using approximate intermediate-channel salience to build channel masks without dense computation. It outperforms training-free baselines across ten LLMs, achieving up to 1.99x end-to-end decoding speedup at 70% sparsity.

This paper introduces SparseSpec-L, a training-free self-speculative decoding framework for long-context LLM inference that uses a dynamically sparsified and recallable KV cache along with an entropy-based controller for speculation length, achieving up to 2.79x speedup over autoregressive decoding.

ReTopK is a training-free method that accelerates dynamic Top-K sparse attention for long-context LLMs by reusing historical query–support pairs, avoiding full-context scoring and global Top-K selection. It achieves up to 3.07x speedup over Exact Top-K with only 0.50% perplexity increase at 128K context.

RAG-HAR+ is a retrieval-first, cost-optimized extension of RAG-HAR for human activity recognition from wearable sensors. It uses a retrieval designer agent and majority voting to reduce LLM usage while maintaining accuracy, and demonstrates feasibility for edge deployment.

Introduces V-Steer, a training-free inference-time method that edits cached value vectors to restore instruction hierarchy in language models, raising primary constraint accuracy from under 18% to 92% on controlled benchmarks with negligible overhead.

IRIS is a training-free framework that uses frozen large language models to construct reusable identity representations for entities in knowledge graphs, enabling efficient entity alignment across different KGs without pair-dependent processing.

Proposes Co-E, a training-free system that synchronizes graph and text memory for multi-hop question answering, improving over comparable training-free baselines on six benchmarks.

This paper presents the first comparative evaluation of training-free methods for personalizing toxicity sensitivity in language models at inference time, showing that all methods reduce alignment error by 28-47% but reveal a trade-off between alignment, personalization, and language quality.

OmniScope is a training-free token compression framework for omnimodal LLMs that estimates audio and video relevance separately using the query as a shared anchor, achieving up to 3.53x prefill speedup and over 15% GPU memory reduction with minimal accuracy loss.

Introduces DWT-Fusion, a training-free framework using discrete wavelet analysis of token log-probabilities for detecting LLM-generated text, achieving strong AUROC results on multiple datasets.