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ARXIV:2606.02871 · AGENTS · SUBMITTED 03 JUN · 20:44 UTC · FRESHNESS FRESH
ARXIV:2606.02871AGENTSSUBMITTED 03 JUN · 20:44 UTCFRESHNESS FRESHDongwon Jung · Peng Shi · Yi Zhang · Junshan Zhang · Muhao Chen · arXiv
ALAR is a dual-mode framework for LLM agents that reduces token usage by using compact latent reasoning for routine turns and explicit chain-of-thought only when necessary, improving efficiency without sacrificing…
Opportunity summary
Pain ALAR is a dual-mode framework for LLM agents that reduces token usage by using compact latent reasoning for routine turns and explicit chain-of-thought only when necessary, improving efficiency without sacrificing accuracy.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
ALAR is a dual-mode framework for LLM agents that reduces token usage by using compact latent reasoning for routine turns and explicit chain-of-thought only when necessary, improving efficiency without sacrificing accuracy. Current LLM agents…
Large reasoning models improve performance by generating extended chain-of-thought (CoT) reasoning, but this behavior becomes inefficient when applied to LLM agents. Current LLM agents often generate verbose textual reasoning at every decision step and…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Large reasoning models improve performance by generating extended chain-of-thought (CoT) reasoning, but this behavior becomes inefficient when applied to LLM agents. Code availability is…
Agents moved forward this cycle; last verified June 2026. Public score 7.0/10. Production flags indicate code availability.
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ALAR is a dual-mode framework for LLM agents that reduces token usage by using compact latent reasoning for routine turns and explicit chain-of-thought only when necessary, improving efficiency without sacrificing…
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10.48550/arXiv.2606.02871ALAR is a dual-mode framework for LLM agents that reduces token usage by using compact latent reasoning for routine turns and explicit chain-of-thought only when necessary, improving efficiency without sacrificing accuracy.
Abstract
Large reasoning models improve performance by generating extended chain-of-thought (CoT) reasoning, but this behavior becomes inefficient when applied to LLM agents. Current LLM agents often generate verbose textual reasoning at every decision step and allocate reasoning effort nearly uniformly across turns, leading to substantial inefficiency in multi-turn agentic trajectories. We propose Adaptive Latent Agentic Reasoning (ALAR), a dual-mode framework that uses compact latent reasoning for routine turns and selectively escalates to explicit chain-of-thought when deeper deliberation is needed. ALAR learns latent reasoning by using the agent's actions as supervision anchors and is further optimized to use latent reasoning when it is sufficient for task success and reserve explicit CoT for harder decisions. Experiments on agentic search and tool-use benchmarks show that ALAR maintains comparable or better task accuracy while substantially reducing generated tokens by up to 43.6% in search and 84.6% in tool use. These results demonstrate that ALAR improves the accuracy-efficiency trade-off of LLM agents by reducing unnecessary textual reasoning while preserving explicit deliberation for harder decision steps.
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PROBLEM
ALAR is a dual-mode framework for LLM agents that reduces token usage by using compact latent reasoning for routine turns and explicit chain-of-thought only when necessary, improving efficiency without sacrificing accuracy. Current LLM agents often generate verbose textual reaso...
METHOD
Large reasoning models improve performance by generating extended chain-of-thought (CoT) reasoning, but this behavior becomes inefficient when applied to LLM agents. Current LLM agents often generate verbose textual reasoning at every decision step and allocate reasoning effort...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Large reasoning models improve performance by generating extended chain-of-thought (CoT) reasoning, but this behavior becomes inefficient when applied to LLM agents. Code availability is flagged in the pr...
WHY NOW
Agents moved forward this cycle; last verified June 2026. Public score 7.0/10. Production flags indicate code availability.
{"file name": "input.pdf", "number of pages": 13, "author": "Dongwon Jung; Peng Shi; Yi Zhang; Junshan Zhang; Muhao Chen", "title": "Adaptive Latent Agentic Reasoning", "creation date": null, "modification date": null
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ALAR is a dual-mode framework for LLM agents that reduces token usage by using compact latent reasoning for routine turns and explicit chain-of-thought only when necessary, improving efficiency without sacrificing accuracy.
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