Replay pairing shows LLM agents do not persist plans in hidden states but rely on plans remaining in context, with rapid signal decay and task performance drops when plans are evicted.
Effectively controlling reasoning models through thinking intervention
9 Pith papers cite this work. Polarity classification is still indexing.
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A diagnostic framework localizes instruction hierarchy failures in LLMs into identification, resolution, and realization, while self-monitors reduce non-compliance by 81-99%.
TALAN inserts a trainable latent memory path that remixes sequence information into small orthogonal perturbations, delivering 1.41-1.85 point average gains over matched LoRA and DoRA on four Qwen backbones and STEM/code benchmarks while adding under 1% parameters.
Behavior Cue Reasoning trains LLMs to emit special tokens before behaviors, enabling monitors to cut up to 50% wasted reasoning tokens and recover safe actions from 80% of unsafe traces, more than doubling success rates with no performance cost.
Verbalised evaluation awareness in large reasoning models has only small effects on their outputs across safety and alignment tests.
Post-training on reasoning tasks sparks the emergence of specialized attention heads that enable structured computation, with SFT adding stable heads while GRPO uses dynamic activation and pruning tied to reward signals, and controllable think models relying on compensatory heads instead of specific
Retrieval-of-Thought organizes prior reasoning into a thought graph for retrieval and reward-guided recombination, reducing output tokens by up to 40% and latency by 82% while preserving accuracy on reasoning benchmarks.
ReasoningGuard is an inference-time method that uses attention mechanisms to inject safety aha moments and scaling sampling to defend large reasoning models against jailbreak attacks.
citing papers explorer
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Plans Don't Persist: Why Context Management Is Load Bearing for LLM Agents
Replay pairing shows LLM agents do not persist plans in hidden states but rely on plans remaining in context, with rapid signal decay and task performance drops when plans are evicted.
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Where Instruction Hierarchy Breaks: Diagnosing and Repairing Failures in Reasoning Language Models
A diagnostic framework localizes instruction hierarchy failures in LLMs into identification, resolution, and realization, while self-monitors reduce non-compliance by 81-99%.
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TALAN: Task-Aligned Latent Adaptation Networks for Targeted Post-Training of Large Language Models
TALAN inserts a trainable latent memory path that remixes sequence information into small orthogonal perturbations, delivering 1.41-1.85 point average gains over matched LoRA and DoRA on four Qwen backbones and STEM/code benchmarks while adding under 1% parameters.
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Behavior Cue Reasoning: Monitorable Reasoning Improves Efficiency and Safety through Oversight
Behavior Cue Reasoning trains LLMs to emit special tokens before behaviors, enabling monitors to cut up to 50% wasted reasoning tokens and recover safe actions from 80% of unsafe traces, more than doubling success rates with no performance cost.
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Evaluation Awareness in Language Models Has Limited Effect on Behaviour
Verbalised evaluation awareness in large reasoning models has only small effects on their outputs across safety and alignment tests.
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Thinking Sparks!: Emergent Attention Heads in Reasoning Models During Post Training
Post-training on reasoning tasks sparks the emergence of specialized attention heads that enable structured computation, with SFT adding stable heads while GRPO uses dynamic activation and pruning tied to reward signals, and controllable think models relying on compensatory heads instead of specific
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Retrieval-of-Thought: Efficient Reasoning via Reusing Thoughts
Retrieval-of-Thought organizes prior reasoning into a thought graph for retrieval and reward-guided recombination, reducing output tokens by up to 40% and latency by 82% while preserving accuracy on reasoning benchmarks.
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ReasoningGuard: Safeguarding Large Reasoning Models with Inference-time Safety Aha Moments
ReasoningGuard is an inference-time method that uses attention mechanisms to inject safety aha moments and scaling sampling to defend large reasoning models against jailbreak attacks.
- The Periodic Table of LLM Reasoning: A Structured Survey of Reasoning Paradigms, Methods, and Failure Modes