Conversational agents are good at recall. But recall only reacts and an agent that answers only what it is asked can never raise what the user never thought to mention. We study the other direction which we call prospective memory. A background agent keeps a small set of pins in the assistant’s context before any question arrives and each pin must be retired by the right authority. A constraint from chat, such as a stolen bike, ends only on the user’s word. One from a city feed, such as a suspended tram line, ends only on the feed’s end message, whatever its forecast said. We release a benchmark of 60 days, ten users and 1,802 graded decisions in a mobility assistant, with automatic answer keys for pinning, forgetting, recommendation and habit discovery. Capture, at least of the templated reveals this benchmark renders, is the easy half. A dense 8B model captures events almost as well as a 397B/17B sparse one and what separates the two is forgetting. The small model hoards and invents. The large model’s one systematic failure is a release cascade, asked to rewrite its whole pin table, it treats any release signal as a reason to shorten it. Asked instead to name its removals as explicit operations, it names only the right ones, every swept pin survives, and required session recall rises from 0.77 to 0.99, from 0.80 to 1.00 on the events both runs captured.

Long-term conversational AI requires two distinct capabilities: remembering what was discussed and adapting to how a user’s preferences evolve over time. We argue these operate on different timescales and require separate but interoperable architectures. AgenticLOCI is a five-agent framework built on this premise: session retrieval fuses a short-term semantic ranker with a long-term keyword scorer via rank fusion, while personalization is maintained through a versioned deliberation-state memory that accumulates decision-episode snapshots across interactions. We evaluate on three benchmarks using Qwen 2.5-7B-Instruct. On LoCoMo, the AgenticLOCI Backbone achieves F1 = 0.342 versus BM25 = 0.285 while consuming 7.3× fewer input tokens per item. On LongMemEval-S, item-level analysis confirms that the two retrieval layers solve architecturally distinct sub-problems; at 7B scale the keyword layer leads, a reversal expected to resolve at larger model sizes where LLM ranking is more reliable. On PersonaMem, all configurations perform within a narrow band, establishing that memory benefit is conditional on context-budget pressure. The longitudinal personalization layer is evaluated on a synthetic mobility benchmark with known user trajectories, keeping 97.6% of stable preference fields unchanged and making warranted updates with 58.3% precision against an ablation baseline. We demonstrate the full framework as a personalized urban mobility assistant for Magdeburg.

It remains unclear whether agentic augmentation (iterative tool use, code execution, or retrieved memory) improves over single-pass VLM inference for medical image interpretation. We present a systematic quantitative evaluation of the Recursive Language Model (RLM) framework on a multimodal medical task, comparing three pipeline architectures (P1 direct vision, P2 iterative RLM tool use, P3 model-written code agent) across four retrieval-augmented memory conditions on 100 CheXpert Plus chest X-rays, scoring with CheXbert (14-label classification) and GREEN (radiology-specific LLM judge). Three findings emerge: (1) direct vision outperforms both agentic variants; the RLM loop consumes 7-9× more tokens without adding task-relevant information when its only tool calls the same VLM; (2) retrieval consistently helps, but the optimal pool size depends on metric and pipeline: top-5 is the sweet spot for direct-vision report quality (GREEN +37%), while 24 retrieved cases further benefit pathology classification (CheXbert +50%) and can improve GREEN for agentic pipelines; and (3) qualitative analysis shows code agents with a pretrained classifier succeed when it returns confident pathology scores but fail on normal cases where it returns noise-floor outputs, illustrating that tool quality, not framework complexity, determines outcome.