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pith:O5LD6WDX

pith:2026:O5LD6WDXIQJXS7F5ZHFTOR2B7F
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Teaching Robots to Interpret Social Interactions through Lexically-guided Dynamic Graph Learning

Mathieu Chollet, Tanaya Guha, Tongfei Bian

SocialLDG is a multi-task framework using language models for lexical priors and dynamic graphs to model evolving task affinities among six social interaction tasks, claiming SOTA results on two public HRI datasets plus scalability without forgetting.

arxiv:2604.10895 v3 · 2026-04-13 · cs.HC · cs.RO

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\pithnumber{O5LD6WDXIQJXS7F5ZHFTOR2B7F}

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3 Author claim open · sign in to claim
4 Citations open
5 Replications open
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Claims

C1strongest claim

SocialLDG achieves state-of-the-art performance on two challenging human-robot social interaction datasets available publicly. Second, it supports strong task scalability by learning new tasks seamlessly without catastrophic forgetting. Finally, benefiting from explicit modelling task affinity, it offers insights on how different interactions unfolds in time and how the internal states and observable actions influence each other in human decision making.

C2weakest assumption

Our premise is that these states arise from the same underlying socio-cognitive process and influence each other dynamically.

C3one line summary

SocialLDG is a multi-task framework using language models for lexical priors and dynamic graphs to model evolving task affinities among six social interaction tasks, claiming SOTA results on two public HRI datasets plus scalability without forgetting.

Receipt and verification
First computed 2026-05-20T00:05:44.666074Z
Builder pith-number-builder-2026-05-17-v1
Signature Pith Ed25519 (pith-v1-2026-05) · public key
Schema pith-number/v1.0

Canonical hash

77563f58774413797cbdc9cb374741f94dfa4630202d7ed03af6419b86446792

Aliases

arxiv: 2604.10895 · arxiv_version: 2604.10895v3 · doi: 10.48550/arxiv.2604.10895 · pith_short_12: O5LD6WDXIQJX · pith_short_16: O5LD6WDXIQJXS7F5 · pith_short_8: O5LD6WDX
Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/O5LD6WDXIQJXS7F5ZHFTOR2B7F \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 77563f58774413797cbdc9cb374741f94dfa4630202d7ed03af6419b86446792
Canonical record JSON
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    "cross_cats_sorted": [
      "cs.RO"
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    "license": "http://creativecommons.org/publicdomain/zero/1.0/",
    "primary_cat": "cs.HC",
    "submitted_at": "2026-04-13T01:56:00Z",
    "title_canon_sha256": "c82852f6836d50e8f529841c65f2dbb06258d757fb51cf82b5c4601725ff05f8"
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