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cs.LG2026
Where Activation Sparsity and KV-Cache Sparsity Cross in LLM Decoding
Jungseob Lee, Seungyoon Lee, Seongtae Hong +2
At each step, decoding one sequence with a large language model rereads the projection weights, whose traffic is fixed, and the key-value (KV) cache, whose traffic grows with conte…
cs.LG2026
Flat Score, Amplified Failures: How the Error Budget Masks Damage in Quantized LLM Agents
Jiwon Jang, Kisu Yang, Heuiseok Lim +1
Post-training quantization to 4-bit weights is widely reported to be nearly lossless. We test this claim for multi-turn, tool-calling agents, where it now matters most. On -be…
cs.LG2026
Evidential Transformation Network: Turning Pretrained Models into Evidential Models for Post-hoc Uncertainty Estimation
Yongchan Chun, Chanhee Park, Jeongho Yoon +2
Pretrained models have become standard in both vision and language, yet they typically do not provide reliable measures of confidence. Existing uncertainty estimation methods, such…