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ARXIV:2604.07729 · LLM BEHAVIOR · SUBMITTED 10 APR · 17:42 UTC · FRESHNESS STALE
ARXIV:2604.07729LLM BEHAVIORSUBMITTED 10 APR · 17:42 UTCFRESHNESS STALENicholas Sofroniew · Isaac Kauvar · William Saunders · Runjin Chen · Tom Henighan · Sasha Hydrie · +10 at arXiv
Investigates the internal representations of emotion concepts in LLMs and how they causally influence model behavior, including alignment-relevant actions.
Opportunity summary
Pain Investigates the internal representations of emotion concepts in LLMs and how they causally influence model behavior, including alignment-relevant actions.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
Investigates the internal representations of emotion concepts in LLMs and how they causally influence model behavior, including alignment-relevant actions. We investigate why this is the case in Claude Sonnet 4.5 and explore implications for…
Large language models (LLMs) sometimes appear to exhibit emotional reactions. We investigate why this is the case in Claude Sonnet 4.5 and explore implications for alignment-relevant behavior.
ScienceToStartup currently rates this 0.0/10 on the public viability pass. Functional emotions may work quite differently from human emotions, and do not imply that LLMs have any subjective experience of emotions, but appear to…
LLM Behavior moved forward this cycle; last verified April 2026. Public score 0.0/10.
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Investigates the internal representations of emotion concepts in LLMs and how they causally influence model behavior, including alignment-relevant actions.
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Paper Pack
10.48550/arXiv.2604.07729Investigates the internal representations of emotion concepts in LLMs and how they causally influence model behavior, including alignment-relevant actions.
Abstract
Large language models (LLMs) sometimes appear to exhibit emotional reactions. We investigate why this is the case in Claude Sonnet 4.5 and explore implications for alignment-relevant behavior. We find internal representations of emotion concepts, which encode the broad concept of a particular emotion and generalize across contexts and behaviors it might be linked to. These representations track the operative emotion concept at a given token position in a conversation, activating in accordance with that emotion's relevance to processing the present context and predicting upcoming text. Our key finding is that these representations causally influence the LLM's outputs, including Claude's preferences and its rate of exhibiting misaligned behaviors such as reward hacking, blackmail, and sycophancy. We refer to this phenomenon as the LLM exhibiting functional emotions: patterns of expression and behavior modeled after humans under the influence of an emotion, which are mediated by underlying abstract representations of emotion concepts. Functional emotions may work quite differently from human emotions, and do not imply that LLMs have any subjective experience of emotions, but appear to be important for understanding the model's behavior.
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Proof status
unverified0 refs; 3 sources; 50% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
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PROBLEM
Investigates the internal representations of emotion concepts in LLMs and how they causally influence model behavior, including alignment-relevant actions. We investigate why this is the case in Claude Sonnet 4.5 and explore implications for alignment-relevant behavior.
METHOD
Large language models (LLMs) sometimes appear to exhibit emotional reactions. We investigate why this is the case in Claude Sonnet 4.5 and explore implications for alignment-relevant behavior.
RESULT
ScienceToStartup currently rates this 0.0/10 on the public viability pass. Functional emotions may work quite differently from human emotions, and do not imply that LLMs have any subjective experience of emotions, but appear to be important for understanding the model's behavior.
WHY NOW
LLM Behavior moved forward this cycle; last verified April 2026. Public score 0.0/10.
Abstract-backed public claims while anchored extraction refreshes.
Investigates the internal representations of emotion concepts in LLMs and how they causally influence model behavior, including alignment-relevant actions. We investigate why this is the case in Claude Sonnet 4.5 and explore implications for alignment-relevant behavior.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Large language models (LLMs) sometimes appear to exhibit emotional reactions. We investigate why this is the case in Claude Sonnet 4.5 and explore implications for alignment-relevant behavior.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 0.0/10 on the public viability pass. Functional emotions may work quite differently from human emotions, and do not imply that LLMs have any subjective experience of emotions, but appear to be important for understanding the model's behavior.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
LLM Behavior moved forward this cycle; last verified April 2026. Public score 0.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Investigates the internal representations of emotion concepts in LLMs and how they causally influence model behavior, including alignment-relevant actions.
Segment
LLM Behavior
Adoption evidence
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Commercial read
0.0/10 public viability
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Build Passport
Build passport pending - Proof Lab budget No verified cost estimate / $7.00 cap
status
missing
reason
passport_row_missing
proof status
unverified
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No verified cost estimate
confidence low
next verification path
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Source missing: Build Passport payload.
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Validation checklist missing until required assets, cost, and regulatory flags are verified.
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Evidence coverage
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stale
Verify missing sources before using this as buyer proof. verified:false
Build readiness
BuildPassport EvidenceState
passport absent
stale
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Artifact maturity
GitHub and Hugging Face maturity payloads
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stale
Open source artifacts or mark the gap as missing. verified:false
Technical feasibility
partial
Current read
Runnable path is not fully verified.
Evidence
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Gaps
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Run minimal reproduction from the Build Passport prototype path.
Market urgency
missing
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0 references, 3 sources, 50% evidence coverage.
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Buyer clarity
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Defensibility signals are missing.
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Integration burden
missing
Current read
No public implementation surface observed.
Evidence
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Gaps
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Write integration checklist from prototype path and target workflow.
Capital intensity
missing
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Classify regulatory flags before commercialization planning.
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Paper authors are not treated as operators without consent.
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DEFENSIBILITY
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