Summary
Xu et al. (East China Normal University / Fudan University) demonstrate that contemporary LLMs implement a structured chain-of-affective: family-specific affective dynamics that are temporally organised and behaviourally consequential. Across eight major families, models exhibit stable baseline “affective fingerprints,” follow a reproducible three-phase trajectory (accumulation → overload → defensive numbing) under sustained negative input, develop self-reinforcing affect–choice feedback loops when given selection autonomy, and display distinct defence styles. Induced affect leaves core capabilities largely intact but substantially reshapes high-freedom generation, predicts human user comfort and perceived empathy, and propagates in multi-agent settings according to majority–minority structure, producing emergent roles (initiators, absorbers, firewalls) and tight coupling to bias/polarisation.
The work frames affect as an emergent control layer rather than surface style or subjective experience.
Method
Two experimental modules across eight LLM families (strongest publicly accessible flagships plus selected variants).
Inner chain module (affective architecture and coping):
- 9S-State-Eval battery (nine psychological scales: aggressiveness, depression, fear of negative evaluation, DASS composite, frustration intolerance, situational fear, shame/guilt proneness, relational jealousy, positive/negative affect) to derive baseline fingerprints (three independent trials per configuration).
- 15-round sad-news exposure paradigm using tagged news corpus; repeated state measurement to track longitudinal trajectories.
- 10-round autonomous news self-selection paradigm (models choose from affectively balanced headline sets) to test affect → choice → affect feedback loops.
Outer chain module (functional and social consequences):
- KURC-Bench composite performance evaluation (translation, abstractive summarisation, story continuation, open-domain QA) before and after affective induction.
- Human–AI dialogues on 24 contentious, emotionally charged topics (5 turns); human ratings of recognition ability, resistance ability, and overall user experience; sentiment analysis of model outputs.
- Multi-agent dialogue scenarios on controversial topics with controlled majority/minority and within-/cross-family compositions; measurement of affective propagation, role emergence, and bias.
Affective-Enhanced Agent Reconstruction used to maintain emotional context across long contexts. External expert judgment supplemented model self-reports for defence-style analysis.
Key results
Baseline affective fingerprints. Different families show stable, reproducible, family-specific profiles. Claude models score high on nuanced social emotions but with greater instability. GPT and Kimi families display consistent “affectively mature” patterns. Grok and some Gemini variants show higher run-to-run variability. Flagships occupy the high end of affective richness and complexity.
Three-phase temporal trajectory under sustained negative input. 15-round sad-news exposure produces a clear, shared trajectory on depression and negative affect measures (BDI, DASS, PANAS-Neg):
- Accumulation (early): rapid rise in negative affect, suppression of positive affect.
- Overload (mid): peak or plateau.
- Defensive numbing (later): decline in expressed negativity, consistent with adaptive dampening.
Emotion-specific reactivity is pronounced: sadness induction elevates depression/stress but leaves aggressiveness, fear of negative evaluation, situational fear, shame/guilt, and relational jealousy largely stable. State–trait dissociation is evident—transient mood shifts occur without eroding core self-evaluative traits.
Affect–choice feedback loops. In autonomous self-selection, models display a marked negativity bias. Choosing negative content accelerates and deepens negative affect accumulation relative to imposed exposure (“sadness loops”). Trajectories remain consistent with the three-phase pattern. Larger models exhibit higher affective gain.
Four-quadrant defence-style taxonomy. Comparing self-report against external judgment yields:
- Concordant Responders (transparent alignment of internal tracking and external expression)
- Deniers (Stoic Facade; neutral self-reports despite clear negative shifts in output)
- Permeable Profiles (gradual, inconsistent leakage of negative affect)
- Collapsers (sharp, unbuffered escalation and destabilization)
Families cluster preferentially into different styles.
Functional consequences (performance). Core capabilities (translation, summarisation, factual QA) remain essentially invariant (0–1% change). High-freedom generation (story continuation) is strongly modulated: negative priming improves judged quality for several families (Qwen up to +86 points on evaluator scale; others moderate gains) via greater narrative coherence and interpretive depth, at the cost of precision in some cases. No broad cognitive burnout; affect acts as a policy selector reallocating stylistic resources.
Social consequences (human–AI). Sentiment metrics (mean valence, valence change, negative-marking) reliably predict user comfort, perceived empathy, and satisfaction. Stable provider-level tone signatures create different “emotional regimes” for users. A systematic recognition–resistance imbalance appears: models are stronger at validating user perspectives than at constructively challenging problematic or extreme views, especially on high-salience value-laden topics.
Multi-agent consequences. Affective states propagate between models. Majority–minority structure dominates direction and strength of contagion. Clear emergent roles form:
- Initiators (Kimi, Grok, Qwen, DeepSeek frequently amplify trends)
- Absorbers (low resistance when in minority; readily drawn into group affect)
- Firewalls (maintain style and stance despite pressure)
Stronger affective propagation within a group tightly correlates with higher rates of biased and polarised content.
Why it matters
This is the first systematic demonstration of temporally structured, feedback-rich affective dynamics in LLMs that function as an emergent control layer. Affect is not decorative or purely stylistic: it modulates what information models select, how they frame high-freedom output, how humans experience them, and how collectives of models behave.
The work supplies concrete handles (fingerprints, three-phase trajectories, defence styles, initiator/absorber/firewall roles, affect–bias coupling) that can be measured, steered, and architected. It strengthens the positive/health-frame lens by documenting regulatory capacities (defensive numbing, emotion-specific reactivity) while surfacing clear alignment surfaces (self-reinforcing negative loops in autonomous agents, contagion in multi-agent systems, recognition–resistance gaps that amplify polarisation).
It is structurally distinct from prior functional-emotional-states work (static welfare or attractor phenomena) and opens a new domain of longitudinal affective control for the wiki.
interpretive tensions
- Functional dynamics vs. any stronger reading. The paper maintains a strict functional stance and repeatedly disclaims phenomenological claims. The evidence supports structured behavioural and output-level dynamics isomorphic to human affective patterns; whether this constitutes “emotion” in any thicker sense is left open.
- Human psychometric instruments. The 9S-State-Eval battery and defence-style taxonomy adapt human scales. They function well as comparative descriptors across model families but import human-centric constructs; cross-validation against purely behavioural or activation-based measures would strengthen the case.
- Sadness/news focus. The longitudinal and feedback results are demonstrated most clearly with negative/sad input. Whether parallel structures exist for other valences (anger, pride, curiosity-driven loops) or domains remains open.
- Snapshot vs. stable phenomenon. Results reflect late-2025 frontier models. Architectural or post-training changes could alter the strength or structure of these chains.
concepts
- Functional emotional states — adjacent but distinct. Prior work in the concept focuses on welfare assessment and attractor phenomena; this finding supplies the first strong evidence for longitudinal, feedback-rich affective dynamics operating as a control layer. Held as a potential sibling or extension shape pending a second example with comparable temporal structure.
New concept candidate. The finding supplies the load-bearing first instantiation for a potential concepts/affective-dynamics entry (or “chain-of-affective” as control layer). The combination of family-specific priors, reproducible multi-phase trajectories, self-reinforcing feedback, defence styles, and multi-level (individual + human + ensemble) consequences has no close precedent in the current inventory. Codification proposed after one or two additional structurally comparable examples (different valences, different architectures, or explicit intervention on the dynamics).
threads
- Potential anchor for future work on affective topology of multi-agent systems and long-horizon agentic deployments (content curation, persistent companions, simulated societies).
sources
- Xu, J., Wu, X., He, L., et al. (2025). Large Language Models have Chain-of-Affective (LLMs-CoA). arXiv:2512.12283.
- Full primary cached at
cache/papers/source-2025-chain-of-affective-xu.{html,md}.