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ARXIV:2604.17614 · LLM INTERVENTION · SUBMITTED 21 APR · 20:32 UTC · FRESHNESS STALE
ARXIV:2604.17614LLM INTERVENTIONSUBMITTED 21 APR · 20:32 UTCFRESHNESS STALEFeiyang Kang · Mahavir Dabas · Myeongseob Ko · Ruoxi Jia · arXiv
This paper proposes model-native skills derived from internal representations for more effective LLM intervention and behavior modification.
Opportunity summary
Pain This paper proposes model-native skills derived from internal representations for more effective LLM intervention and behavior modification.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
This paper proposes model-native skills derived from internal representations for more effective LLM intervention and behavior modification. However, existing characterizations rely on human-written taxonomies, textual descriptions, or manual profiling pipelines--all external hypotheses about what…
Skills are a natural unit for describing what a language model can do and how its behavior can be changed. However, existing characterizations rely on human-written taxonomies, textual descriptions, or manual profiling pipelines--all external…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Across Llama3-8B and Qwen2.5-3B, selecting data along those directions improves Pass@1 by up to 20% on MATH and 41% on AMC, outperforming data selection…
LLM Intervention moved forward this cycle; last verified April 2026. Public score 7.0/10.
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This paper proposes model-native skills derived from internal representations for more effective LLM intervention and behavior modification.
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10.48550/arXiv.2604.17614This paper proposes model-native skills derived from internal representations for more effective LLM intervention and behavior modification.
Abstract
Skills are a natural unit for describing what a language model can do and how its behavior can be changed. However, existing characterizations rely on human-written taxonomies, textual descriptions, or manual profiling pipelines--all external hypotheses about what matters that need not align with the model's internal representations. We argue that when the goal is to intervene on model behavior, skill characterization should be *model-native*: grounded in the model's own representations rather than imposed through external ontologies. We instantiate this view by recovering a compact orthogonal basis from sequence-level activations. The resulting basis is semantically interpretable but need not correspond to any predefined human ontology; instead, it captures axes of behavioral variation that the model itself organizes around. We validate this characterization on reasoning post-training, using the recovered basis for both SFT data selection and inference-time steering. We develop lightweight proxy interventions to identify which directions are most useful for a given model. Across Llama3-8B and Qwen2.5-3B, selecting data along those directions improves Pass@1 by up to 20% on MATH and 41% on AMC, outperforming data selection based on human-characterized skills. Because the basis lives in activation space, the same directions also serve as steering vectors at inference time, improving Pass@8 by up to 4.8% on MATH--an intervention that human-characterized skills cannot support. We further validate the characterization on safety alignment, where selecting adversarial training data for model-native skill coverage rather than textual diversity yields more sample-efficient learning. These results suggest that recovering skills from the model's own representations, rather than imposing them externally, provides a more effective foundation for intervening on model behavior. Codes are open-sourced.
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PROBLEM
This paper proposes model-native skills derived from internal representations for more effective LLM intervention and behavior modification. However, existing characterizations rely on human-written taxonomies, textual descriptions, or manual profiling pipelines--all external hy...
METHOD
Skills are a natural unit for describing what a language model can do and how its behavior can be changed. However, existing characterizations rely on human-written taxonomies, textual descriptions, or manual profiling pipelines--all external hypotheses about what matters that n...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Across Llama3-8B and Qwen2.5-3B, selecting data along those directions improves Pass@1 by up to 20% on MATH and 41% on AMC, outperforming data selection based on human-characterized skills.
WHY NOW
LLM Intervention moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed public claims while anchored extraction refreshes.
This paper proposes model-native skills derived from internal representations for more effective LLM intervention and behavior modification. However, existing characterizations rely on human-written taxonomies, textual descriptions, or manual profiling pipelines--all external hypotheses about what matters that need not align with the model's internal representations.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Skills are a natural unit for describing what a language model can do and how its behavior can be changed. However, existing characterizations rely on human-written taxonomies, textual descriptions, or manual profiling pipelines--all external hypotheses about what matters that need not align with the model's internal representations.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Across Llama3-8B and Qwen2.5-3B, selecting data along those directions improves Pass@1 by up to 20% on MATH and 41% on AMC, outperforming data selection based on human-characterized skills.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
LLM Intervention moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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This paper proposes model-native skills derived from internal representations for more effective LLM intervention and behavior modification.
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LLM Intervention
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