TALON: Improving Large Language Model Cognition with Tactility-Vision Fusion
Bibliographic record
Abstract
Current Multimodal Large Language Models (MLLMs) mainly focus on vision and language modalities, often overlooking the integration of other senses, such as tactile perception. In this paper, we present Improving Language Model Cognition with Tactility-Vision Fusion (TALON) to achieve tactility-vision fusion. We first develop a high-density flexible array tactile sensor, Hand-Scan, and deployed it on a data glove. Using the glove, we collect tactile information, and with a camera, we gather visual information to construct the TALON dataset, containing both tactile and visual data. We then train our TALON model using this dataset, achieving modality alignment. Our experiments demonstrate that the TALON model exhibits outstanding recognition capabilities with an accuracy rate of 99.45%, surpassing solely vision-language training (97.58%) and solely tactility-language training (70.47%). Particularly in complex gesture recognition tasks, the accuracy reached 98.82% (+3.06% over vision-language, +18.38% over tactility-language), showcasing the near-perfect performance and proving the effectiveness of tactility-vision fusion.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".