Papers with PLC
CROP: Contextual Region-Oriented Visual Token Pruning (2025.emnlp-main)
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| Challenge: | Existing VLMs process entire images, leading to excessive visual tokens . redundant image information also introduces a large number of visual token, requiring much higher memory and computation in VLM. |
| Approach: | They propose a framework to prune visual tokens using localization and pruning . they propose CROP to locate local image regions relevant to the query . |
| Outcome: | The proposed framework outperforms existing visual token pruning methods on a wide range of tasks. |
Watch Out Your Industrial Copilots: Stealthy Backdoor Attack Against LLM-Based PLC Code Generation (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) are being used to generate PLC code from natural language. |
| Approach: | They propose a stealthy backdoor attack framework targeting LLM-based PLC code generation . they incorporate six malicious logic injection patterns and a pipeline to refine stealthiness . |
| Outcome: | The proposed framework achieves 82.92% success rate while remaining stealthy . it bypasses quality validation and is difficult to detect . |
Beyond Compromise: Pareto-Lenient Consensus for Efficient Multi-Preference LLM Alignment (2026.findings-acl)
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| Challenge: | Recent approaches to align LLMs with diverse human values are based on static linear scalarization or rigid gradient projection . however, these approaches often sacrifice potential global Pareto improvements to avoid transient local trade-offs. |
| Approach: | They propose a game-theoretic framework that reimagines alignment as a dynamic negotiation process. |
| Outcome: | The proposed framework breaks the deadlock between static linear scalarization and rigid gradient projection . it allows the model to escape local degradation and explore the distal Pareto-optimal frontier . |