Argus coordinates a Navigator and multiple Searchers via an evidence graph for deep research, reporting average gains of 5.5 points with one Searcher and 12.7 points with eight parallel Searchers across eight benchmarks, reaching 86.2 on BrowseComp with 64 Searchers.
SealQA: Raising the Bar for Reasoning in Search-Augmented Language Models
9 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
We introduce SealQA, a new challenge benchmark for evaluating SEarch-Augmented Language models on fact-seeking questions where web search yields conflicting, noisy, or unhelpful results. SealQA comes in three flavors: (1) Seal-0 (main) and (2) Seal-Hard, which assess factual accuracy and reasoning capabilities, with Seal-0 focusing on the most challenging questions where chat models (e.g., GPT-4.1) typically achieve near-zero accuracy; and (3) LongSeal, which extends SealQA to test long-context, multi-document reasoning in "needle-in-a-haystack" settings. Our evaluation reveals critical limitations in current models: Even frontier LLMs perform poorly across all SealQA flavors. On Seal-0, frontier agentic models equipped with tools like o3 and o4-mini achieve only 17.1% and 6.3% accuracy, respectively, at their best reasoning efforts. We find that advanced reasoning models such as DeepSeek-R1-671B and o3-mini are highly vulnerable to noisy search results. Notably, increasing test-time compute does not yield reliable gains across o3-mini, o4-mini, and o3, with performance often plateauing or even declining early. Additionally, while recent models are less affected by the "lost-in-the-middle" issue, they still fail to reliably identify relevant documents in LongSeal when faced with numerous distractors. To facilitate future work, we release SealQA at huggingface.co/datasets/vtllms/sealqa.
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verdicts
UNVERDICTED 9representative citing papers
APEX-MEM uses property graphs with temporal events, append-only storage, and an agentic retrieval system to reach 88.88% accuracy on LOCOMO QA and 86.2% on LongMemEval, outperforming prior session-aware methods.
A fine-tuning policy trains small language models to search reliably and use evidence, improving multi-hop QA performance by 15-17 points to reach large-model levels.
ExpSeek shifts web agents to self-triggered step-level experience seeking via entropy thresholds, delivering 9.3% and 7.5% absolute gains on Qwen3-8B and 32B models across four benchmarks.
MiroThinker shows that scaling agent-environment interactions via reinforcement learning lets a 72B open-source model reach up to 81.9% on GAIA and approach commercial performance on research benchmarks.
Tool-augmented LLM reasoning incurs a protocol-induced performance tax that can exceed tool benefits under semantic noise, partially mitigated by a lightweight gate called G-STEP.
Guardian-as-an-Advisor prepends risk labels and explanations from a guardian model to queries, improving LLM safety compliance and reducing over-refusal while adding minimal compute overhead.
EvoSkill evolves agent skills via failure analysis and Pareto frontier selection, raising exact-match accuracy 7.3% on OfficeQA and 12.1% on SealQA with 5.3% zero-shot transfer to BrowseComp.
Kimi K2.5 combines joint text-vision training with an Agent Swarm parallel orchestration framework to reach claimed state-of-the-art results on coding, vision, reasoning, and agent tasks while cutting latency up to 4.5 times.
citing papers explorer
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Argus: Evidence Assembly for Scalable Deep Research Agents
Argus coordinates a Navigator and multiple Searchers via an evidence graph for deep research, reporting average gains of 5.5 points with one Searcher and 12.7 points with eight parallel Searchers across eight benchmarks, reaching 86.2 on BrowseComp with 64 Searchers.
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APEX-MEM: Agentic Semi-Structured Memory with Temporal Reasoning for Long-Term Conversational AI
APEX-MEM uses property graphs with temporal events, append-only storage, and an agentic retrieval system to reach 88.88% accuracy on LOCOMO QA and 86.2% on LongMemEval, outperforming prior session-aware methods.
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Search, Do not Guess: Teaching Small Language Models to Be Effective Search Agents
A fine-tuning policy trains small language models to search reliably and use evidence, improving multi-hop QA performance by 15-17 points to reach large-model levels.
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ExpSeek: Self-Triggered Experience Seeking for Web Agents
ExpSeek shifts web agents to self-triggered step-level experience seeking via entropy thresholds, delivering 9.3% and 7.5% absolute gains on Qwen3-8B and 32B models across four benchmarks.
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Are Tools All We Need? Unveiling the Tool-Use Tax in LLM Agents
Tool-augmented LLM reasoning incurs a protocol-induced performance tax that can exceed tool benefits under semantic noise, partially mitigated by a lightweight gate called G-STEP.
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Guardian-as-an-Advisor: Advancing Next-Generation Guardian Models for Trustworthy LLMs
Guardian-as-an-Advisor prepends risk labels and explanations from a guardian model to queries, improving LLM safety compliance and reducing over-refusal while adding minimal compute overhead.
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EvoSkill: Automated Skill Discovery for Multi-Agent Systems
EvoSkill evolves agent skills via failure analysis and Pareto frontier selection, raising exact-match accuracy 7.3% on OfficeQA and 12.1% on SealQA with 5.3% zero-shot transfer to BrowseComp.
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Kimi K2.5: Visual Agentic Intelligence
Kimi K2.5 combines joint text-vision training with an Agent Swarm parallel orchestration framework to reach claimed state-of-the-art results on coding, vision, reasoning, and agent tasks while cutting latency up to 4.5 times.