General-purpose machine translation benchmarks such as FLORES-200 have reached a saturation regime on Chinese-English pairs, where modern large language models cluster within a narrow band of high scores. Across 22 systems, FLORES-200 zh-en GEMBA scores fall in a 7.87-point range with a standard deviation of 2.29, which compresses the separation between systems on knowledge-intensive domains such as finance, healthcare, law, and science and technology. We introduce HardMTBench, a difficulty-aware diagnostic benchmark for bidirectional Chinese-English domain translation. HardMTBench covers 12 domains and contains 10000 hand-curated source sentences with reference translations, packaged as 20000 directional test items. A three-stage construction pipeline builds a domain-balanced candidate pool of 84566 pairs, applies an LLM-based multi-signal judge over knowledge density, translation difficulty, terminology load and reference correctness, and assembles the final test set under a hardness fusion rule with per-domain quotas. Across 22 systems spanning general LLMs, commercial engines and specialised MT models, HardMTBench widens the cross-system GEMBA range by roughly a factor of two over FLORES-200, induces visible rank reorderings, and exposes domain-specific terminology and knowledge weaknesses that quality-only metrics tend to flatten.

Long-term memory for an LLM agent requires more than retrieving a relevant passage. Many existing systems address belief revision, causal connections and cross-domain abstraction primarily through storage and retrieval mechanisms, limiting their ability to support implicit personalisation as a user’s preferences evolve. We present DPCM, which organises memory into a capability hierarchy spanning raw inputs, atomic facts, belief trajectories, identity, domain schemas, prospective intentions and cross-domain patterns. A synchronous daytime writer (SYSTEM 1) records revisions as doubly linked supersedes chains; an asynchronous nighttime engine (SYSTEM 2) induces schemas and intentions, then uses cross-domain collisions to derive core schemas. Dual-process theory motivates this separation between online encoding and offline abstraction without implying that agent memory reproduces human cognition. Across single-run evaluations on LongMemEval, PersonaMem and PersonaMem-v2, SYSTEM 2 yields its largest observed gain on implicit cross-session inference (+5.20 on PersonaMem-v2) and its smallest on the recall-oriented LongMemEval aggregate (+0.33). When the inference budget is limited to 8k tokens and the full history no longer fits, DPCMfull exceeds the long-context baseline by 10.5–17.9 points across both backbones.