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Notes on efficiency
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EvoAlloc: A Self-Evolving Resource Allocation Agent for Efficient Program Evolution
EvoAlloc is described as a self-evolving resource-allocation agent for LLM-based program evolution. It consolidates prior search and allocation outcomes into reusable experience, revises its allocation strategy, and…
HARISSA: Inference-Time Self-Checks for Efficient and Safe Local Language Model Deployment
The preprint uses hidden states before generation and at the end of an answer to predict correctness for local-model deployment. HARISSA fine-tunes both states and uses a single policy to choose among increasingly…
Learning to Stop without Learning to Stop: Self-Supervised Confidence Training Improves Reasoning Efficiency
The preprint studies whether reasoning efficiency can improve without directly optimizing length or adding an inference-time stopping mechanism. It fine-tunes models to predict confidence at intermediate points in…
Jev-Mobile: Jev as an Executor for Mobile GUI Agents
Jev-Mobile separates low-frequency vision-language-model planning from high-frequency lightweight execution. The VLM supplies local goals, the accessibility tree defines a structured action space, and Jev repeatedly…
CliffCompaction: Cost-Efficient Compaction for Long-Horizon Coding Agents
CliffCompaction is an autocompaction technique for long-horizon coding agents that reduces context cost while preserving or improving benchmark performance. It only truncates or drops content rather than rephrasing it…
CompKV: Compensation-Aware KV Selection for Long-Context LLM Inference
CompKV selects token blocks jointly with the downstream compensation mechanism for sparse attention rather than choosing tokens only by attention mass. The preprint derives a block-level residual approximation based on…
Grow the Harness, Not the Context: From Strategy-Free Scaffolds to Reusable Specialist Agents
Growing Harness learns the agent harness itself from a strategy-free scaffold. Function-level traces localize failures, an optimizer repairs bounded code surfaces, and a held-out success-first gate rolls back repairs…
JEV-as-a-Judge: Accept When Confident, Escalate When Unsure
The preprint evaluates a decision-only judge as an economical first pass and escalates low-confidence cases to stronger evaluation. It compares the judge with 16 generative and reward-model judges using blinded human…
Harness-Zero: Harness Distillation via Agent-as-Harness
Harness-Zero uses a domain- or instance-optimized harness as training-time guidance, then transfers its behavior into model weights so the specialized harness can be removed at deployment. An agent-as-harness corrects…
An Empirical Study of Harness Design for Coding Agents
The paper varies planning, action space, and context management in a fixed coding harness and evaluates 176 matched settings across four models on SWE-Bench Verified and Terminal-Bench 2.1. It reports that context…
Score Centering Stabilizes Off-policy Reinforcement Learning
The preprint attributes instability in large-language-model reinforcement learning under training-inference mismatch to persistent drift between engines. It derives an additive score-centering correction that cancels…
SoL-Pi: Recursively Scaling Auto-Research Loops for Efficient Agent Harness
SoL-Pi applies recursive self-improvement at the harness layer across many environments. The abstract describes mechanisms for action execution, context compaction, observation handling, and delegated reading; on a…
HiLS-Attention: Hierarchical Sparse Attention Done Right
Tencent's Hunyuan team released official code for HiLS-Attention, a chunk-based sparse attention method meant to make very long-context language models faster to run while keeping accuracy, learning which chunks of…
Reachability-Aware Pretraining for Efficient Target-Oriented Path Exploration in Temporal Knowledge Graph Reasoning
The paper proposes a new method called RAPTOR for temporal knowledge graph (TKG) reasoning under the extrapolation setting. This method aims to improve the efficiency of target-oriented path exploration by using…
Do AI Agents Know When a Task Is Simple? Toward Complexity-Aware Reasoning and Execution
Researchers have proposed a method called E3 (Estimate, Execute, Expand) to improve the efficiency of large language model (LLM) agents. E3 aims to estimate the difficulty of a task, execute a minimum viable path, and…
DT-Guard: Intent-Driven Reasoning-Active Training for Reasoning-Free LLM Safety Guardrail
Researchers present DT-Guard, a content safety guardrail model for large language models in open-world applications. The model uses a Reasoning-Active Training, Reasoning-Free Inference paradigm and aims to improve…
Quantization Inflates Reasoning: Token Inflation as a Hidden Cost of Low-Bit Reasoning Models
A study finds that using low-bit quantization to reduce the cost of large language models can lead to longer chains of thought, which offsets the expected per-token speedup in reasoning tasks. The study introduces a…
GRINQH: Graded Input-based Quantization Hierarchy for Efficient LLM Generation
The paper presents a method called GRINQH (GRaded INput-based Quantization Hierarchy) to accelerate the decoding process of language models during inference, particularly beneficial for edge-computing settings. The…
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