Transformers Remember First, Forget Last: Dual-Process Interference in LLMs
2026-03-04T19:50:14Z•81daf13397f0f04e960a53b764107d60016d8c6fec59c484b153d23093eb994e
CoCoALLM memoryLoRAMuonRecPhotoBench benchmarkQuaSIDRAIEReFeed datasetSODASemantic IDsTARSETiny-Critic RAGagentic RAGclinical QAgenerative recommendationmultimodal embeddingsoptimizer (Muon)parameter-efficient modelsproactive interferencequery rewritingrecommender systemsretrieval-augmented generationretroactive interferencetest-time adaptationtransformers
What happened
Collection of arXiv submissions (Mar 2026) focused on LLM memory dynamics and a broad set of advances in recommendation, retrieval, and multimodal systems. Key findings: transformers exhibit a strong, universal primacy bias (proactive interference dominates retroactive interference), with implications for how context is preserved and updated. Recommendation research introduces new optimizers (MuonRec), semantic ID learning (QuaSID), region-aware incremental editing with LoRA (RAIE), distributional alignment for generative recommenders (SODA), hierarchical/preference-aware generative recommend-
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_cs_ir
- Record identifier
- 81daf13397f0f04e960a53b764107d60016d8c6fec59c484b153d23093eb994e
- Enrichment time
- 2026-03-04T19:50:14Z
- AI-assisted enrichment
- Yes
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