arXiv AI
Jun 16
EMS: Multi-Agent Voting via Efficient Majority-then-Stopping
arXiv:2604. 02863v2 Announce Type: replace Abstract: Majority voting is the standard for aggregating multi-agent responses into a final decision.
Agentic Test-Time Scaling for WebAgents
arXiv:2602. 12276v2 Announce Type: replace Abstract: Test-time scaling has become a standard way to improve performance and boost reliability of neural network models.
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arXiv AI
Jun 2
PETS: A Principled Framework Towards Optimal Trajectory Allocation for Efficient Test-Time Self-Consistency
arXiv:2602. 16745v2 Announce Type: replace-cross Abstract: Test-time scaling can improve model performance by aggregating stochastic reasoning trajectories.
arXiv AI
Jun 2
Demystifying Multi-Agent Debate: The Role of Confidence and Diversity
arXiv:2601. 19921v2 Announce Type: replace-cross Abstract: Multi-agent debate (MAD) is widely used to improve large language model (LLM) performance through test-time scaling, yet recent work shows that vanilla MAD often underperforms simple majority vote despite higher computational cost.
arXiv Machine Learning
Jun 18
GrowthHacker: Automated Off-Policy Evaluation Optimization Using Code-Modifying LLM Agents
arXiv:2511. 00802v2 Announce Type: replace-cross Abstract: With data-driven development now widely adopted, online A/B testing is an established method for measuring the effects of new technologies.
arXiv AI
Jun 12
MARS: Margin-Adversarial Risk-controlled Stopping for Parallel LLM Test-time Scaling
arXiv:2606. 12935v1 Announce Type: new Abstract: Parallel test-time scaling samples many reasoning traces and majority-votes their answers, improving LLM accuracy but requiring traces to run to completion, incurring substantial computational overhead.
arXiv AI
Jul 24
InferenceBench: A Benchmark for Open-Ended LLM Inference Optimization by AI Agents
arXiv:2607. 20468v1 Announce Type: new Abstract: AI agents are increasingly used to automate research and development tasks, yet existing benchmarks typically evaluate them on prescribed workflows or narrow action spaces.
arXiv AI
Jun 6
AdaMEM: Test-Time Adaptive Memory for Language Agents
arXiv:2606. 05684v1 Announce Type: new Abstract: A central challenge for language agents is utilizing past experience to adapt to dynamic test-time conditions.
arXiv AI
Jul 7
Council Mode: A Heterogeneous Multi-Agent Consensus Framework for Reducing LLM Hallucination and Bias
arXiv:2604. 02923v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have demonstrated advanced capabilities but often suffer from factual inaccuracies (hallucinations) and systematic biases.
Hugging Face Trending Papers
Jun 11
MARS: Margin-Adversarial Risk-controlled Stopping for Parallel LLM Test-time Scaling
Parallel test-time scaling samples many reasoning traces and majority-votes their answers, improving LLM accuracy but requiring traces to run to completion, incurring substantial computational overhead. We observe that probing partial traces at intermediate checkpoints can extract current answers without disrupting generation, revealing an evolving aggregate vote.
arXiv AI
1d ago
Budget-Aware Tool Use Enables Effective Agent Scaling
arXiv:2511. 17006v2 Announce Type: replace Abstract: Scaling test-time computation has been extended from language model reasoning to tool-augmented agents, where scaling involves not only thinking in tokens but also acting via tool calls that directly constrain environmental interaction.
arXiv AI
Jul 7
Attributing Emergence in Million-Agent Systems
arXiv:2605. 11404v2 Announce Type: replace Abstract: Large language models (LLMs) can simulate human-like reasoning and decision-making in individual agents.
arXiv AI
Jul 3
PACE: A Proxy for Agentic Capability Evaluation
arXiv:2607. 02032v1 Announce Type: new Abstract: Evaluating LLM agents on benchmarks like SWE-Bench and GAIA can be expensive, time-consuming, and requires complex infrastructure.