Benyou Wang

Long-horizon agents must preserve past experience before knowing what future queries will require. Compact memory makes growing histories efficient to reuse by distilling experience into reusable abstractions, while raw history preserves fine-grained evidence for faithful grounding. Because future queries require different levels of detail, the appropriate memory granularity cannot be reliably fixed when memory is written. We therefore argue that memory granularity should be determined when the future information need becomes known. We introduce \textbf{TierMem}, a provenance-linked two-tier memory framework that allocates memory granularity according to evidence sufficiency. TierMem retrieves compact memory by default, uses a learned router to identify when finer-grained evidence is required, and follows provenance links to recover relevant raw evidence. Recovered evidence can be consolidated into compact memory for future reuse. Across two long-horizon memory benchmarks, TierMem recovers much of the quality enabled by raw evidence while substantially reducing inference cost. Further analyses show that provenance improves evidence recovery and that consolidation progressively increases the share of queries served by compact memory.

Multilingual large language models (LLMs) are essential for cross-lingual applications, yet pruning them with mixed-language calibration induces *cross-lingual interference*, disproportionately degrading low-resource and script-diverse languages. We introduce ***Lang-Prune***, a drop-in extension to structured pruning that computes importance per language on small calibration sets and aggregates via a Max operator---preserving any structure critical to at least one language. On *aya-expanse-8b* across nine typologically diverse languages, Lang-Prune reduces average perplexity by 62\% at 70\% sparsity relative to mixed-data pruning, and outperforms monolingual pruning on average under the same calibration budget. Lang-Prune generalizes across model families and scales: its advantage amplifies with model size, reaching $4.1\times$ perplexity reduction at *Qwen3-14B* and exhibiting strong zero-shot transfer to out-of-distribution languages. Post-training experiments further show preserved adaptation capacity, with pruned models reaching 59.6\% vs. 50.6\% accuracy on Belebele. Lang-Prune modifies only the importance estimation and aggregation step, preserving LLM-Pruner's coupled-structure mechanics and requiring no additional parameters or retraining.