arXiv AI
Jun 11
Soft-Prompt Tuning for Fair and Efficient LLM Benchmark Evaluation
arXiv:2606. 12117v1 Announce Type: cross Abstract: Benchmark scores often misrepresent a large language model's (LLM's) knowledge, because they rely, e.
CRAFT: Cost-aware Refinement And Front-aware Tuning of Prompts
arXiv:2606. 04661v1 Announce Type: cross Abstract: Prompts tuned for accuracy often grow long, raising inference cost on every model call.
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arXiv AI
Aug 12
Optimize Cheap, Deploy Strong: Cost-Aware Cross-Tier Transfer for Evolutionary Optimization
arXiv:2608. 10694v1 Announce Type: cross Abstract: Evolutionary optimization of LLM prompts and agentic programs (e.
arXiv AI
Jun 11
APEX: Automated Prompt Engineering eXpert with Dynamic Data Selection
arXiv:2606. 11459v1 Announce Type: cross Abstract: Large Language Models are highly sensitive to prompt formulation, necessitating automatic prompt optimization to unlock their full potential.
arXiv Machine Learning
1d ago
PRO-Bid: Pareto-Prioritized Regret Optimization for Constraint-Aware Generative Auto-Bidding
arXiv:2602. 08261v2 Announce Type: replace Abstract: Auto-bidding systems strive to maximize marketing value while maintaining high compliance with efficiency constraints, such as Target Cost-Per-Action (CPA).
arXiv AI
Jul 9
Cost-Effective Agent Harnesses for Abstract Reasoning and Generalization on ARC-AGI-1
arXiv:2607. 06764v1 Announce Type: new Abstract: Recent progress on ARC-AGI-1 from disclosed architectures has come broadly from two regimes: heavy test-time compute over frontier models (evolutionary search, exhaustive sampling, extended chain-of-thought), or benchmark-specific training in which small models are fine-tuned on ARC data, often with task-specialized architectures.
arXiv AI
Jun 4
Not All Errors Are Equal: Consequence-Aware Reasoning Compute Allocation
arXiv:2606. 04402v1 Announce Type: new Abstract: Modern reasoning models can allocate different amounts of test-time computation, such as thinking tokens, model calls, or compute budget, to different tasks.
arXiv AI
1d ago
CAPO: Constraint-Aware Prompt Optimization for LLM Agents
arXiv:2608. 16068v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as agents that rely on system prompts to use tools and complete tasks.
arXiv AI
Jul 14
OS-Pruner: Pruning Chains-of-Thought of Reasoning Models via Optimal Stopping
arXiv:2607. 11089v1 Announce Type: new Abstract: Large Language Models (LLMs) have achieved remarkable success in complex reasoning tasks through Chain-of-Thought (CoT) prompting.
arXiv AI
Jun 19
FAPO: Fully Autonomous Prompt Optimization of Multi-Step LLM Pipelines
arXiv:2606. 19605v1 Announce Type: cross Abstract: Multi-step LLM pipelines fail through interactions among retrieval, reasoning, and formatting steps, so prompt-only optimization can miss bottlenecks in the chain.
arXiv AI
Jul 28
Efficiency Matters in Autonomous Research
arXiv:2607. 24647v1 Announce Type: new Abstract: AI-driven autonomous research (AR) systems are becoming increasingly effective across a broad range of tasks.
Hugging Face Trending Papers
Jul 13
OS-Pruner: Pruning Chains-of-Thought of Reasoning Models via Optimal Stopping
Large Language Models (LLMs) have achieved remarkable success in complex reasoning tasks through Chain-of-Thought (CoT) prompting. However, these models often exhibit "computational overthinking," generating redundant reasoning steps that increase latency and cost without improving accuracy.
arXiv AI
Aug 6
COMPAS: Difficulty-Aware Joint Search for Optimizing Code Generation
arXiv:2608. 04336v1 Announce Type: cross Abstract: Code generation systems make each LLM call with a model, a prompt, and decoding settings.