training-free

SESSE is a training-free framework that decomposes holistic LLM-as-judge evaluations into structured sub-questions, enabling better interpretability and diagnosis of label ambiguity while achieving competitive performance with fine-tuned models.

FlowEvo is a training-free framework that enables large language model agents to co-evolve reusable skills and workflows at inference time, achieving state-of-the-art accuracy and efficiency across benchmarks like ALFWorld, HumanEval, and GSM8K.

SparsePR is a training-free method that accelerates video transformers by using response-coupled partitioning and probe-fitted residual reconstruction to reduce attention error while maintaining generation quality.

The paper proposes dLLM-SetScore, a training-free framework using discrete masked-diffusion language models for multi-label text classification, achieving competitive performance with minimal validation data.

This paper proposes Activation-Prune-Merge (APM), a training-free framework for cross-scale fusion that improves smaller language models using larger donors without semantic alignment, achieving performance gains on multiple benchmarks.

Second Thought is a training-free framework that runs auxiliary reasoning branches in parallel during LLM agent action-observation waits to reduce sequential decoding and turn counts without harming accuracy.

This paper identifies a privacy vulnerability in RL-trained multimodal large reasoning models, which can leak sensitive facts in their reasoning traces even after unlearning, and proposes LEMUR, a training-free inference-time framework that uses entropy dynamics to detect and suppress such leakage.

Proposes Ripple-Pivot Search, a training-free decoding method for diffusion large language models that proactively commits mid-entropy pivot positions to reduce uncertainty and accelerate parallel decoding, achieving 4-10x speedup.

This paper presents a unified global-to-local paradigm for video anomaly detection, introducing a training-free framework (GtS) and a tool-augmented agentic reasoning method with reinforcement learning, along with a new benchmark VAGU-T and metric JeAUG.

CORA-Diff is a training-free method that accelerates diffusion language model inference by using native confidence and persistence signals to accept residual positions early, skipping redundant dense denoising passes while preserving task quality.

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.