SEATauBench is the first agent benchmark for SEA languages, finding that performance holds for language-only changes but degrades sharply with full domain localization.
M ulti WOZ - A Large-Scale Multi-Domain W izard-of- O z Dataset for Task-Oriented Dialogue Modelling
10 Pith papers cite this work, alongside 312 external citations. Polarity classification is still indexing.
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Pith papers citing it
312
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verdicts
UNVERDICTED 10representative citing papers
A Gaussian information-gain metric in embedding space quantifies semantic progress in dialogues via uncertainty reduction and shows competitive agreement with human judgments on MT-Bench and UltraFeedback.
Derives a blockwise resolvent-style attention operator that exploits structured sparsity for subquadratic O(n^{4/3}d) entity tracking while matching dense accuracy.
SCICONVBENCH is a new benchmark evaluating LLMs on multi-turn disambiguation and inconsistency resolution for task formulation in computational science, with frontier models reaching only 52.7% success on fluid mechanics disambiguation cases.
LongMemEval benchmarks long-term memory in chat assistants, revealing 30% accuracy drops across sustained interactions and proposing indexing-retrieval-reading optimizations that boost performance.
Using CORE-Bench as a case study, the paper shows that saturated benchmarks can still deliver insights on efficiency, reliability, model-scaffold differences, and human collaboration even after accuracy plateaus, and introduces improved benchmark versions plus a small randomized experiment demonstra
GBC treats multi-agent LLM workflows as differentiable graphs to enable token-level attribution and targeted optimization, with reported gains on MultiWOZ and τ-bench.
IAP uses RL to train LLMs to explicitly infer and apply implicit user intent in single-turn personalized QA, achieving ~7.5% average macro-score gains over baselines on LaMP-QA.
Proposes GODR, a framework-neutral runtime pattern treating goals and their lifecycle as first-class objects for complex, interruptible multi-domain dialogues.
Workplace agent success on WorkBench rose from 43% to 98% over two years with sharply reduced harmful actions.
citing papers explorer
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SEATauBench: Adapting Tool-Agent-User Evaluation Into Low-Resource Southeast Asian Languages
SEATauBench is the first agent benchmark for SEA languages, finding that performance holds for language-only changes but degrades sharply with full domain localization.
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Measuring Semantic Progress in Multi-turn Dialogue via Information Gain
A Gaussian information-gain metric in embedding space quantifies semantic progress in dialogues via uncertainty reduction and shows competitive agreement with human judgments on MT-Bench and UltraFeedback.
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Structured-Sparse Attention for Entity Tracking with Subquadratic Sequence Complexity
Derives a blockwise resolvent-style attention operator that exploits structured sparsity for subquadratic O(n^{4/3}d) entity tracking while matching dense accuracy.
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SCICONVBENCH: Benchmarking LLMs on Multi-Turn Clarification for Task Formulation in Computational Science
SCICONVBENCH is a new benchmark evaluating LLMs on multi-turn disambiguation and inconsistency resolution for task formulation in computational science, with frontier models reaching only 52.7% success on fluid mechanics disambiguation cases.
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LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory
LongMemEval benchmarks long-term memory in chat assistants, revealing 30% accuracy drops across sustained interactions and proposing indexing-retrieval-reading optimizations that boost performance.
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Life After Benchmark Saturation: A Case Study of CORE-Bench
Using CORE-Bench as a case study, the paper shows that saturated benchmarks can still deliver insights on efficiency, reliability, model-scaffold differences, and human collaboration even after accuracy plateaus, and introduces improved benchmark versions plus a small randomized experiment demonstra
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GBC: Gradient-Based Connections for Optimizing Multi-Agent Systems
GBC treats multi-agent LLM workflows as differentiable graphs to enable token-level attribution and targeted optimization, with reported gains on MultiWOZ and τ-bench.
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Training LLMs with Reinforcement Learning for Intent-Aware Personalized Question Answering
IAP uses RL to train LLMs to explicitly infer and apply implicit user intent in single-turn personalized QA, achieving ~7.5% average macro-score gains over baselines on LaMP-QA.
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From Task-Guided Conversational Graphs to Goal-Oriented Dialogue Runtimes
Proposes GODR, a framework-neutral runtime pattern treating goals and their lifecycle as first-class objects for complex, interruptible multi-domain dialogues.
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WorkBench Revisited: Workplace Agents Two Years On
Workplace agent success on WorkBench rose from 43% to 98% over two years with sharply reduced harmful actions.