Researcher profile

Zhihan Guo

Zhihan Guo contributes to research discovery and scholarly infrastructure.

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Published work

2 published item(s)

preprint2026arXiv

Insular intracranial activity identifies multiple facial expressions via diverse, intermixed temporal patterns at the single-contact level

How neural representations in the insular cortex support emotional processing remains poorly understood, and the extent to which the insula is specialized for disgust processing remains debated. We recorded stereoelectroencephalography data from the insula while human subjects with implanted electrode contacts performed a facial emotion recognition task involving disgusted, fearful, angry, sad, neutral, and happy expressions. Expression category specificity of insular activity was assessed via pairwise comparisons of within- and between-category pattern similarities, capturing both the shape and scale of event-related potentials (ERPs) and event-related spectral perturbations (ERSPs; theta to high-gamma frequency ranges). Insular activity successfully identified all investigated expressions, mediated by diverse ERP responses intermixed across the insula. In contrast to the marked heterogeneity of insula ERP responses, the fusiform face area exhibited convergent ERP responses across expressions and contacts, with ERSPs also contributing substantially to expression identification. These findings not only elucidate the insula's neural mechanisms underlying facial emotion perception, but also establish a potential single-contact-level neural substrate for how the insula leverages its heterogeneous response profiles to act as a key hub for versatile cognitive and emotional functions.

preprint2026arXiv

Remember the Decision, Not the Description: A Rate-Distortion Framework for Agent Memory

Long-horizon language agents must operate under limited runtime memory, yet existing memory mechanisms often organize experience around descriptive criteria such as relevance, salience, or summary quality. For an agent, however, memory is valuable not because it faithfully describes the past, but because it preserves the distinctions between histories that must remain separated under a fixed budget to support good decisions. We cast this as a decision-centric rate-distortion problem, measuring memory quality by the loss in achievable decision quality induced by compression. This yields an exact forgetting boundary for what can be safely forgotten, and a memory-distortion frontier characterizing the optimal tradeoff between memory budget and decision quality. Motivated by this decision-centric view of memory, we propose DeMem, an online memory learner that refines its partition only when data certify that a shared state would induce decision conflict, and prove near-minimax regret guarantees. On both controlled synthetic diagnostics and long-horizon conversational benchmarks, DeMem yields consistent gains under the same runtime budget, supporting the principle that memory should preserve the distinctions that matter for decisions, not descriptions.