{"id":"ee86d332-536c-4442-b268-d91c151007f8","arxiv_id":"2606.20047","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":6.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":0,"one_line_summary":"PACMS uses submodular context selection as a pluggable engine to choose relevant context from conversation, memory, and tool outputs instead of recency truncation.","lead":"The paper proposes PACMS, a submodular optimization method for selecting relevant items from a unified pool of conversation turns, memory entries, and tool outputs when assembling prompts for LLM agents. A smart generalist might read it to see a potential fix for the common failure of recency truncation in long agent sessions.","discovery_kind":"new_method","skeptic_critique":{"model":"grok-4.3","headline":"Submodular function must capture marginal relevance for arbitrary queries without query-specific engineering or information loss","rationale":"The reader's weakest_assumption is precisely the load-bearing condition. No other internal inconsistency is visible from the supplied text, and the abstract-only limitation prevents checking whether the paper actually supplies a general construction or merely assumes one exists.","tokens_in":1712,"tokens_out":296,"duration_ms":19884,"concrete_test":"Extract the exact submodular objective and greedy algorithm from §3–4; apply the identical function (no re-tuning) to a held-out set of 20 sessions containing both early critical facts and recent distractors; measure recall of the critical facts versus recency baseline. If recall drops below baseline on >30% of sessions, the generality assumption fails.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim requires a single, fixed submodular objective that scores any mixed pool (memory entries + turns + tool outputs) by marginal gain at prompt-assembly time. The abstract states this is done 'by relevance' but supplies no construction for the set function, no proof that diminishing returns hold across query types, and no argument that the greedy optimizer preserves critical facts when the pool is heterogeneous. If the function must be hand-tuned per domain or query class, the 'pluggable engine' claim collapses to the same engineering burden the paper criticizes in prior methods.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The manuscript proposes PACMS, a pluggable engine for LLM agents that treats memory entries, conversation turns, and tool outputs as a single candidate pool and selects a subset via submodular optimization by relevance at prompt-assembly time, as an alternative to recency truncation or query-blind compression methods.","tokens_in":1815,"tokens_out":255,"duration_ms":10019,"significance":"If a single fixed submodular objective can be constructed that reliably captures marginal relevance across heterogeneous item types for arbitrary queries without query-specific engineering or loss of critical facts, the approach would address a practical bottleneck in long-horizon agent sessions and provide a more principled context-management primitive than current heuristics.","major_comments":[{"comment":"Abstract: the claim that a single submodular set function can score any mixed pool (memory + turns + tool outputs) by marginal gain at prompt-assembly time is load-bearing for the 'pluggable engine' contribution, yet the manuscript supplies no explicit construction of the set function, no argument that diminishing returns hold across query types and item heterogeneity, and no demonstration that the greedy optimizer preserves critical facts when the pool is mixed.","section":"Abstract"}],"minor_comments":[],"recommendation":"major_revision","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for the careful reading and the substantive comment on the core claim of the work. We address the point directly below.","responses":[{"response":"We agree that the abstract claim is load-bearing and that the current manuscript does not supply an explicit construction of the set function, a formal argument for submodularity across heterogeneous item types, or a targeted demonstration that greedy selection preserves critical facts in mixed pools. These elements are required to support the pluggable-engine framing. We will revise the manuscript to add (1) the precise mathematical definition of the submodular objective (a linear combination of query-conditioned relevance, coverage, and diversity terms applied uniformly to the candidate pool), (2) a short subsection arguing why the diminishing-returns property is expected to hold across the three item classes without query-specific re-engineering, and (3) an additional analysis in the experimental section that isolates fact-retention rates when the pool is deliberately mixed. The abstract will be updated to reference these additions. We therefore mark this as a major revision item.","revision_made":"yes","referee_comment":"[Abstract] Abstract: the claim that a single submodular set function can score any mixed pool (memory + turns + tool outputs) by marginal gain at prompt-assembly time is load-bearing for the 'pluggable engine' contribution, yet the manuscript supplies no explicit construction of the set function, no argument that diminishing returns hold across query types and item heterogeneity, and no demonstration that the greedy optimizer preserves critical facts when the pool is mixed."}],"tokens_in":1273,"tokens_out":338,"duration_ms":13580,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The main takeaway is that this paper names a practical gap—recency truncation drops useful early facts while keeping irrelevant recent tool output—but gives no working solution. It frames the fix as submodular selection over one pool of memory entries, turns, and tool results at prompt-assembly time, positioned as a pluggable module distinct from RAG or compression.\n\nWhat it does reasonably is spell out why topic-blind truncation hurts long sessions and why prior methods sit outside the agent's internal context decision. The unified-pool idea is a clean way to state the problem.\n\nThe soft spots are large and central. The abstract contains no set function, no marginal-gain definition, no argument that diminishing returns hold across mixed item types, and no experiments. Without those, there is no way to check whether a single objective can score arbitrary queries without per-domain tuning or dropping critical facts. The stress-test concern is accurate on the supplied text: the pluggable-engine claim rests on exactly that unshown generality.\n\nNo equations, derivations, or code appear, so nothing is formally verified or reproducible. The citation pattern is not an issue because there is little prior work to engage.\n\nThis is for engineers building tool-using agents who already know the recency problem and want a concrete alternative. A reader looking for a method or data will not find it. The work does not yet show the level of engagement or grounding that would justify sending it to referees.","headline":"The paper flags a real context-management problem for LLM agents but supplies no method, function, or results to evaluate the submodular claim.","tokens_in":2301,"tokens_out":367,"would_cite":false,"duration_ms":19153,"reading_group":"no","serious_thinker":"unclear","would_accept_peer_review":false},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"PACMS selects context for LLM agents by optimizing submodular relevance over a single pool of memory entries, conversation turns, and tool outputs.","keywords":["context selection","LLM agents","submodular optimization","memory management","prompt assembly","tool outputs","information retrieval"],"falsifier":"A multi-turn agent session in which the submodular selector omits an early fact required for the correct final answer, while a recency baseline retains it and succeeds.","tokens_in":2622,"feed_emoji":"","tokens_out":608,"duration_ms":19427,"temperature":0.7,"pith_summary":"The paper presents PACMS as a pluggable component that assembles prompts by choosing a subset of available context items according to their marginal contribution to answering the current query. It models the decision as submodular maximization so that the selected set stays within the token limit while covering the most useful information from all sources at once. Current practice relies on recency truncation or external retrieval that ignores the agent's internal pool, which causes agents to lose facts needed across many turns. A reader would care because the method keeps the selection step inside the agent's own prompt-assembly logic rather than requiring separate compression or retrieval stages.","feed_headline":"Submodular selection picks relevant context for LLM agents","feed_subtitle":"Memory entries, turns, and tool outputs are treated as one pool and filtered by marginal relevance instead of recency.","key_machinery":"Submodular context selection, an optimization procedure that maximizes a submodular set function defined over the pooled candidate items subject to a cardinality or token-budget constraint.","core_discovery":"PACMS treats memory entries, conversation turns, and tool outputs as a single candidate pool to be selected from by relevance at the moment the prompt is assembled, using submodular functions to capture the diminishing returns of adding each additional item.","pith_inferences":["The same selection step might be reused when external retrieval results are added to the pool.","Long-running agents could maintain accuracy over dozens of turns without periodic manual compression.","Different submodular functions could be swapped in to emphasize recency, diversity, or cost for particular domains."],"forward_implications":["Prompt assembly becomes query-aware instead of depending on recency or fixed summarization schedules.","Memory entries, turns, and tool outputs compete on equal footing inside one selection step.","The selector can be inserted into existing agent loops without altering their core reasoning or tool-calling logic.","Token budgets are allocated to items that still contribute new relevant information rather than to recent but off-topic material."],"fun_headline_variants":["Submodular picks from unified context pool for agents","PACMS submodularly selects from memory turns and tools","Unified pool filtered by submodular relevance for LLM agents","Submodular selection treats context parts as single pool"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"Submodular functions can be written that reliably measure how much each context item adds to answering an arbitrary query without hand-crafted rules for that query.","fun_headline_variants_meta":{"raw":{"variants":["Submodular picks from unified context pool for agents","PACMS submodularly selects from memory turns and tools","Unified pool filtered by submodular relevance for LLM agents","Submodular selection treats context parts as single pool"]},"model":"grok-4.3","cost_usd":0.01089,"raw_usage":{"total_tokens":4707,"prompt_tokens":648,"num_sources_used":0,"completion_tokens":64,"cost_in_usd_ticks":108903000,"prompt_tokens_details":{"text_tokens":648,"audio_tokens":0,"image_tokens":0,"cached_tokens":64},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":3995,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":648,"tokens_out":64,"duration_ms":29550,"temperature":1.0,"reasoning_tokens":3995,"cache_read_input_tokens":64,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-06-26T15:45:28.226905+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"A multi-turn agent session in which the submodular selector omits an early fact required for the correct final answer, while a recency baseline retains it and succeeds.","supporting_citations":[],"review_version":1}