OS-SPEAR is a new evaluation toolkit that tests 22 OS agents and identifies trade-offs between efficiency and safety or robustness.
Determinants of llm-assisted decision-making
10 Pith papers cite this work. Polarity classification is still indexing.
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QMFOL generates monadic first-order logic tasks with controllable complexity via pattern-based structures and round-trip prover verification, then evaluates six LRMs showing performance drops as logical depth and width increase.
Semi-structured interviews with 13 early adopters in one large tech organization identify multiple framings of transparency and synthesize them into a developer-user-governance multidimensional framework.
Geometry-calibrated conformal abstention lets language models abstain from uncertain queries with finite-sample guarantees on both participation rate and conditional correctness of answers.
RAGRoute introduces a neural router for federated RAG that dynamically selects relevant sources, reducing communication by up to 80.65% and latency by 52.50% while preserving accuracy on three benchmarks.
Reddit analysis shows users detect AI sycophancy through comparisons and consistency checks, apply mitigation prompts, and sometimes seek affirmative responses for support, indicating context-aware design is better than total elimination.
Across 43,200 simulations with five LLMs and five scenarios, model trust in humans aligns with human-like patterns driven by trustworthiness dimensions and is sometimes biased by age, gender, and religion.
Uncertainty-aware fine-tuning with a decision-theory-based loss produces better-calibrated uncertainty estimates than standard training on free-form QA tasks.
citing papers explorer
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OS-SPEAR: A Toolkit for the Safety, Performance,Efficiency, and Robustness Analysis of OS Agents
OS-SPEAR is a new evaluation toolkit that tests 22 OS agents and identifies trade-offs between efficiency and safety or robustness.
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QMFOL: Benchmarking Large Language Model Reasoning via Quantifiable Monadic First-Order Logic Test Case Generation
QMFOL generates monadic first-order logic tasks with controllable complexity via pattern-based structures and round-trip prover verification, then evaluates six LRMs showing performance drops as logical depth and width increase.
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"So There's a Catch-22 Here": How Early Adopters Who Build Multi-Agent LLM Systems Conceptualize Transparency
Semi-structured interviews with 13 early adopters in one large tech organization identify multiple framings of transparency and synthesize them into a developer-user-governance multidimensional framework.
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Geometry-Calibrated Conformal Abstention for Language Models
Geometry-calibrated conformal abstention lets language models abstain from uncertain queries with finite-sample guarantees on both participation rate and conditional correctness of answers.
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Efficient Federated Search for Retrieval-Augmented Generation using Lightweight Routing
RAGRoute introduces a neural router for federated RAG that dynamically selects relevant sources, reducing communication by up to 80.65% and latency by 52.50% while preserving accuracy on three benchmarks.
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User Detection and Response Patterns of Sycophantic Behavior in Conversational AI
Reddit analysis shows users detect AI sycophancy through comparisons and consistency checks, apply mitigation prompts, and sometimes seek affirmative responses for support, indicating context-aware design is better than total elimination.
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A closer look at how large language models trust humans: patterns and biases
Across 43,200 simulations with five LLMs and five scenarios, model trust in humans aligns with human-like patterns driven by trustworthiness dimensions and is sometimes biased by age, gender, and religion.
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Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning
Uncertainty-aware fine-tuning with a decision-theory-based loss produces better-calibrated uncertainty estimates than standard training on free-form QA tasks.
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