Language models are increasingly used to reason over content they were not trained on, such as new documents, evolving knowledge, and user-specific data. A common approach is retrieval-augmented generation (RAG), which stores verbatim documents externally (as chunks) and retrieves only a relevant subset at inference time for an LLM to reason over. However, this results in inefficient usage of test-time compute (LLM repeatedly reasons over the same documents); moreover, chunk retrieval can inject irrelevant context that increases unsupported generation. We propose a human-like non-parametric continual learning framework, where the base model remains fixed, and learning occurs by integrating each new experience into an external semantic memory state that accumulates and consolidates itself continually. We present Panini, which realizes this by representing documents as Generative Semantic Workspaces (GSW) -- an entity- and event-aware network of question-answer (QA) pairs, sufficient for an LLM to reconstruct the experienced situations and mine latent knowledge via reasoning-grounded inference chains on the network. Given a query, Panini only traverses the continually-updated GSW (not the verbatim documents or chunks), and retrieves the most likely inference chains. Across six QA benchmarks, Panini achieves the highest average performance, 5%-7% higher than other competitive baselines, while using 2-30x fewer answer-context tokens, supports fully open-source pipelines, and reduces unsupported answers on curated unanswerable queries. The results show that efficient and accurate structuring of experiences at write time -- as achieved by the GSW framework -- yields both efficiency and reliability gains at read time. Code is available at https://github.com/roychowdhuryresearch/gsw-memory.
Figures & tables
Figure 1: A non-parametric continual learning (NPCL) framework schematics (1) Continual experience : incoming documents are processed asynchronously, potentially by different agents. (2) Individual workspaces : each experience is encoded into a Generative Semantic Workspace (GSW). (3) Continually learned global workspace : GSWs can be continually consolidated by reconciling entities, events, and actions both across and within documents. Extensive ablation studies (see Table 11 ) show that different combinations of LLM models of different sizes for performing different tasks – GSW generation, and retrieval – lead to consistently robust performance. Thus GSW can be used as a shared meta-representation. (4) Reasoning-grounded inference : The goal is to have enough reconciliation –but not exhaustive– so that all latent knowledge supported by the collection of experiences are represented by inference chains/paths .
Figure 2: System overview of PANINI at inference time. Step 1: Planning: A decomposition LLM converts the user query into an ordered sequence of single-hop sub-questions. Step 2: RICR: We perform chain-based retrieval by expanding candidate paths hop-by-hop. The initial seed set is obtained via embedding similarity; therefore, for a query like “Who was Lothair II’s mother?”, retrieval may include both Lothair II and the semantically nearby Lothair I . From these seeds, RICR follows QA edges to propose intermediate entities (e.g., candidate mothers) and incrementally extends partial chains across GSWs. Candidate chains are scored at each hop, and low-scoring paths are pruned. Step 3: Answer Generation: Top-ranked chains are de-duplicated and provided to the final answering LLM.
NQ
PopQA
MuSiQue
2Wiki
HotpotQA
LV-Eval
Queries
1,000
1,000
1,000
1,000
1,000
124
Passages
9,633
8,676
11,656
6,119
9,811
22,849
Table 1: Dataset statistics. We use the same benchmark splits as HippoRAG 2 ( Gutiérrez et al., 2025a ) .
Simple QA
Multi-Hop QA
Retrieval
NQ
PopQA
MuSiQue
2Wiki
HotpotQA
LV-Eval
Avg
No Retrieval
None
52.7
22.7
22.0
36.3
41.0
5.0
29.9
Sparse Retrieval
BM25
56.0
53.0
25.3
35.4
54.2
7.8
38.6
BM25 + reranker
58.2
56.8
29.9
44.1
63.4
8.1
43.4
Table 2: Performance (F1 ↑ ) comparison across six QA benchmarks. Panini achieves the highest average performance (56.06%). Bold = best; underline = second best.
Simple QA
Multi-Hop QA
Avg
Retrieval
NQ
PopQA
MuSiQue
2Wiki
HotpotQA
LV-Eval
Avg Token Count
Chunk Retrieval Methods
Standard Retrieval
701.97
665.40
569.28
544.53
650.35
1099.78
705.27
Structure-Augmented RAG
RAPTOR
1161.7
1100.8
942.1
900.79
1076.1
1820.1
1166.6
GraphRAG
8076.4
7658.9
6554.7
6265.1
7487.0
12661.2
8121.6
Table 3: Average token count (Tokens ↓ ) in generation context across methods. Panini uses 2–30 × fewer tokens than competitive baselines. Bold = best; underline = second best.
MuSiQue Platinum
2Wiki Platinum
Avg
Retrieval
Ans ↑
Unans ↑
Ans ↑
Unans ↑
Ans ↑
Unans ↑
Sparse Retrieval
BM25
35.0
78.4
37.9
79.8
36.4
79.1
BM25 + reranker
42.3
74.5
47.9
78.7
45.1
76.6
Dense Retrieval
Qwen3 (8B)
53.3
63.4
63.5
70.9
58.4
67.2
Table 4: Platinum dataset evaluation with GPT-4o-mini. Ans = answerable, Unans = unanswerable. Bold = best; underline = second best.
Appendix figures & tables24 assets
Supplementary material from the paper’s appendix.
Appendix
Input passage (verbatim)
Title: Lothair II Text: Lothair II (835 – ) was the king of Lotharingia from 855 until his death. He was the second son of Emperor Lothair I and Ermengarde of Tours. He was married to Teutberga (died 875), daughter of Boso the Elder.
GSW Output
Entities (role → states). • Lothair II : person → {deceased, historical figure}; ruler → {king of Lotharingia, medieval period} • 835 : date → {birth year, medieval period} • Lotharingia : location → {kingdom, medieval period} • 855 : date → {start of reign, medieval period} • Emperor Lothair I : person → {historical figure}; ruler → {Carolingian Emperor, medieval period} • Ermengarde of Tours : person → {historical figure}; nobility → {medieval period} • Teutberga : person → {deceased, historical figure}; nobility → {medieval period} • 875 : date → {death year, medieval period} • Boso the Elder : person → {historical figure}; nobility → {medieval period} Verb phrase node Bidirectional QA pairs (with roles/states on answers) king of • Q: Who was the king of Lotharingia? A: Lothair II ( person → {deceased, historical figure}; ruler → {king of Lotharingia, medieval period}) • Q: What was Lothair II the king of? A: Lotharingia ( location → {kingdom, medieval period}) reigned from • Q: When did Lothair II start his reign? A: 855 ( date → {start of reign, medieval period}) • Q: Who started reigning in 855? A: Lothair II ( person → {deceased, historical figure}; ruler → {king of Lotharingia, medieval period}) son of • Q: Who is the son of Emperor Lothair I? A: Lothair II ( person → {deceased, historical figure}; ruler → {king of Lotharingia, medieval period}) • Q: Who is Lothair II the son of? A: Emperor Lothair I ( person → {historical figure}; ruler → {Carolingian Emperor, medieval period}) son of • Q: Who is the son of Ermengarde of Tours? A: Lothair II ( person → {deceased, historical figure}; ruler → {king of Lotharingia, medieval period}) • Q: Who is Lothair II the son of? A: Ermengarde of Tours ( person → {historical figure}; nobility → {medieval period}) married to • Q: Who was Lothair II married to? A: Teutberga ( person → {deceased, historical figure}; nobility → {medieval period}) • Q: Who was married to Teutberga? A: Lothair II ( person → {deceased, historical figure}; ruler → {king of Lotharingia, medieval period}) daughter of • Q: Who is the daughter of Boso the Elder? A: Teutberga ( person → {deceased, historical figure}; nobility → {medieval period}) • Q: Who is Teutberga the daughter of? A: Boso the Elder ( person → {historical figure}; nobility → {medieval period})
Appendix
Figure 3: Example of a per-document Generative Semantic Workspace (GSW). Top: the raw input passage (title + text). Bottom: the corresponding GSW, rendered as (i) entity nodes annotated with roles and states, and (ii) verb-phrase nodes instantiated as bidirectional question–answer (QA) pairs.
Parameter
Non-thinking Mode
Thinking Mode
Temperature
0.6
0.6
Top- p
0.95
0.95
Top- k
20
20
Max tokens
4096
12,288
Repetition penalty
–
1.1
Presence penalty
–
0.3
Appendix
Table 5: Decoding hyperparameters for answer generation with open-source reader models.
Hop Type
Number of Examples
2-hop
494
3hop1
441
3hop2
406
4hop1
225
4hop2
49
4hop3
165
Appendix
Table 6: Distribution of question decomposition training data by hop type.
Retrieval
MuSiQue ↑
2Wiki ↑
HotpotQA ↑
Avg ↑
Sparse Retrieval
BM25
17.07
36.74
48.30
34.04
BM25 + reranker
23.16
43.78
57.67
41.54
Dense Retrieval
Qwen3-Embedding (8B)
40.14
56.50
69.47
55.37
Qwen3-Embedding (8B) + reranker
46.30
58.34
72.04
58.89
Appendix
Table 7: Performance comparison across multi-hop QA benchmarks with Qwen3-4B (no-think mode). Experiments were run on GPT-4.1-mini -generated GSW memories, using Qwen3-4B (no-think mode) as the reader model and Qwen3-4B + LoRA for question decomposition (open-source PANINI pipeline). Bold = best; underline = second best.
Simple QA
Multi-Hop QA
Retrieval
NQ ↑
PopQA ↑
MuSiQue ↑
2Wiki ↑
HotpotQA ↑
Avg ↑
Sparse Retrieval
BM25
45.01
47.61
18.96
29.28
48.80
37.93
BM25 + reranker
53.57
51.27
24.34
38.61
59.95
45.55
Dense Retrieval
Qwen3-Embedding (8B)
59.90
59.74
39.97
55.66
69.26
56.91
Appendix
Table 8: Performance comparison across QA benchmarks with Qwen3-8B(no-think mode). Experiments were run on GPT-4.1-mini -generated GSW memories, using Qwen3-8B (no-think mode) as the reader and Qwen3-8B + LoRA for question decomposition (open-source PANINI pipeline). Bold = best; underline = second best.
MuSiQue Platinum
2Wiki Platinum
Avg
Retrieval
Ans ↑
Unans ↑
Ans ↑
Unans ↑
Ans ↑
Unans ↑
Sparse Retrieval
BM25
20.17
82.35
30.24
88.30
25.21
85.33
BM25 + reranker
27.79
76.47
40.37
83.33
34.08
79.90
Dense Retrieval
Qwen3-Embedding (8B)
40.88
66.01
49.02
75.18
44.95
70.60
Appendix
Table 9: Platinum dataset evaluation with Qwen3-8B (no-think mode). Ans = F1 score on the answerable subset. Unans = refusal accuracy on the unanswerable subset (binary; correct iff the output is the canonical non-answer token N/A after normalization). Experiments were run on GPT-4.1-mini -generated GSW memories, using Qwen3-8B (no-think mode) as the reader and Qwen3-8B + LoRA for question decomposition (open-source PANINI pipeline). Bold = best; underline = second best.
MuSiQue Platinum
2Wiki Platinum
Avg
Retrieval
Ans ↑
Unans ↑
Ans ↑
Unans ↑
Ans ↑
Unans ↑
Sparse Retrieval
BM25
18.04
89.54
28.62
93.97
23.33
91.76
BM25 + reranker
21.71
86.93
36.61
87.23
29.16
87.08
Dense Retrieval
Qwen3-Embedding (8B)
36.89
82.35
47.13
74.47
42.01
78.41
Appendix
Table 10: Platinum dataset evaluation with Qwen3-8B (thinking mode). Ans = F1 score on the answerable subset. Unans = refusal accuracy on the unanswerable subset (binary; correct iff the output is the canonical non-answer token N/A after normalization). Experiments were run on GPT-4.1-mini -generated GSW memories, using Qwen3-8B (thinking mode) as the reader and Qwen3-8B + LoRA for question decomposition (open-source PANINI pipeline). Bold = best; underline = second best.
Model
2Wiki
MuSiQue
Qwen3-8B
55.38
40.24
Qwen3-14B
62.80
44.87
Qwen3-14B (Second Pass)
66.76
48.28
GPT-OSS-120B
70.65
48.50
HippoRAG2 (Qwen3-8B reader)
66.93
45.40
PANINI (Main Exp)
72.4
52.3
Appendix
Table 11: Performance comparison when varying the open-source model used for write-time GSW construction. All open-source models use the same read-time configuration: LoRA-finetuned Qwen3-8B for question decomposition and Qwen3-8B (no-think) as the reader. We also report HippoRAG2 under the same reader model and the PANINI configuration from Table 2 as reference points.
Setting
MuSiQue
2Wiki
Full Panini
52.3
72.4
No QA reranking
18.4
22.2
No dual search (entity-only)
39.5
68.6
No dual search (QA-only)
52.3
72.0
No decomposition
36.8
47.2
Beam width = 10
52.4
72.7
Appendix
Table 12: Ablation study on multi-hop QA (F1 score, 1000 questions).
Setting
MuSiQue
2Wiki
F1
Tokens
F1
Tokens
Beam width = 5
52.3
192
72.4
315
Beam width = 10
52.4
320
72.7
409
Beam width = 3
52.3
143
72.2
231
Beam width = 1
44.6
82
67.3
171
Appendix
Table 13: Ablation study on multi-hop QA (F1 score and average token count, 1000 questions).
Scoring rule
MuSiQue
None (greedy last-hop)
50.81
Similarity
42.32
Combined
48.24
Main (cumulative)
52.3
Appendix
Table 14: Ablation of chain-level scoring on MuSiQue ( F1 , 1,000 questions). “Main” denotes cumulative (geometric-mean) scoring; “Similarity” uses only question–chain embedding similarity; “Combined” linearly interpolates the two ( α=0.5 ); “None” disables chain-level scoring and performs greedy last-hop selection.
Method
Write-time cost
PANINI (Batch)
$48.02
PANINI (Standard, est.)
$96.04
HippoRAG 2
$91.8
Qwen3-Embedding (8B)
$0
Appendix
Table 15: Write-time indexing cost on MuSiQue (11,656 passages). Costs are one-time dataset-level indexing costs. PANINI write-time corresponds to per-document GSW construction with gpt-4.1-mini ; the Batch variant uses the OpenAI Batch API, while the Standard variant is an estimate assuming a 2 × higher cost without the Batch discount. HippoRAG 2 cost is the reported total run cost using GPT-4o-mini with standard API calls as default. Qwen3-Embedding (8B) has zero write-time LLM cost since it performs embedding-only indexing.
Simple QA
Multi-Hop QA
Avg
Retrieval
NQ
PopQA
MuSiQue
2Wiki
HotpotQA
LV-Eval
Avg
Search-R1
47.9
49.7
41.1
64.9
68.6
11.5
47.3
Search-R1 (Dense)
41.6
52.7
45.1
64.6
71.3
18.8
49.0
Search-R1 (Dense + QA)
48.5
51.5
44.6
65.4
66.8
12.0
48.1
Search-R1 (Dense + QA + Retrieval)
48.1
54.0
46.6
67.6
70.0
10.5
49.4
Panini
67.4
57.6
52.3
72.4
71.9
14.8
56.1
Appendix
Table 16: Performance of Search-R1 under different retrieval configurations. All rows use the same Search-R1 agent model and differ only in the retrieval mechanism. Search-R1 uses BM25 over document chunks; Search-R1 (Dense) replaces BM25 with dense embedding retrieval ( Qwen3-Embedding-8B ) over document chunks; Search-R1 (Dense + QA) further replaces document chunks with the structured QA pairs from the GSW; Search-R1 (Dense + QA + Dual Index) additionally incorporates PANINI’s RICR retrieval strategy and retrieves relevant QA pairs. PANINI is included as a reference point and uses the same evaluation setting as in Table 2 .
Query
Method
Failure mode
Failure reason
What year did the publisher of Labyrinth end?
Qwen3-Emb
missing_second_hop
Retrieved passages broadly about Labyrinth and publishing, but missed the bridging evidence required for the second hop.
What is the location of formation of the film company distributing The Boss ?
Qwen3-Emb
missing_second_hop
Over-matched the surface phrase “The Boss” and returned loosely related documents, failing to retrieve the distributor → company-formation link.
Between the state university in the state without North Point Mall and where Edwards won the primary and the university in Fort Hill’s town, which has the more national championships?
Qwen3-Emb
missing_entity
Requires multiple supporting documents; failed to retrieve the intermediate evidence involving North Point Mall, breaking the chain.
Who’s the son of the Italian navigator who explored the eastern coast of the continent Manuel Balbi was born in?
Qwen3-Emb
broken_chains
Retrieved the correct answer passage, but missed the intermediate evidence (continent identification and the relevant navigator), so the chain could not be supported.
What is the name of the famous bridge in the birth city of the composer of Scanderbeg ?
HippoRAG2
retrieval_drift
Drifted to a plausible distractor in the same city (e.g., another famous Venice bridge), and failed to rank the correct bridge-related evidence highest.
Who is the child of the president under whom prohibition occurred?
HippoRAG2
retrieval_drift
Found evidence about prohibition but drifted to nearby presidents/laws, losing the correct intermediate link needed to answer the composed query.
Appendix
Table 17: Representative failure cases for dense embedding retrieval (Qwen3-Embedding-8B) and graph-based retrieval (HippoRAG2).
Failure mode
Example query
What goes wrong
Why it breaks the chain
Missing verb-phrase nodes
What nationality is Arabia (Daughter Of Justin II)’s mother?
The GSW omits the verb-phrase/event capturing Aelia Sophia as an empress of the Byzantine Empire .
Without this relation node, the GSW does not contain any QA pair linking the mother to a country or polity, so the required evidence for determining nationality is never retrieved.
QA-pair construction errors
Who is the paternal grandfather of Dibyasambandh?
The relation daughter of is stored, but QA pairs are only generated in the father → daughter direction; the inverse daughter → father QA is missing.
Since the father entity never appears as the answer to any QA pair, subsequent hops remain conditioned on the daughter entity, blocking retrieval of evidence about the paternal grandfather.
Imperfect question decomposition
When did the country that has the original language of the film named after Vladimir Karalić’s birthplace as a co-official language first attend the Olympics as an independent team?
The decomposer drops co- and queries for official language instead of co-official language.
The weakened constraint retrieves a different country, causing subsequent hops to follow an incorrect chain even if later retrieval is accurate.
Appendix
Table 18: Representative qualitative error cases for PANINI.
Qwen3-Embedding (8B)
Search-R1
HippoRAG 2
PANINI
Indexing Time (min)
1.9
1.9
106.6
100.1
QA Time/Query (sec)
1.3
6.7
4.4
3.3
Appendix
Table 19: Computational resource requirements for end-to-end runtime on MuSiQue (11,656 passages). Indexing time (min) and QA latency per query (sec) for Panini and baselines. QA latency per query is measured with standard (non-batch) API calls.
Figure 4: Prompt used for factual GSW construction from documents.
Figure 5: Prompt used for factual GSW construction from documents (continued).
Figure 6: LLM prompt for 2nd pass GSW Construction.
Figure 7: Prompt used for multi-hop question decomposition into atomic sub-questions.
Figure 8: Oracle-style prompt used for answer generation from retrieved GSW evidence.
Question. What is the place of birth of the performer of song Changed It ? System. Chain-Following Multi-Hop QA with Beam Search Mode. cumulative ( α=0.5 ), reranker = voyage beam_width =5 , chain_top_k =15 , entity_top_k =20 , qa_rerank_top_k =15 Original question. “What is the place of birth of the performer of song Changed It?” Decomposed sub-questions. (1) Q1: “Who performed the song Changed It?” [Retrieval: ✓] → (2) Q2: “What is the place of birth of <ENTITY_Q1> ?” [Retrieval: ✓] Dependencies. Q1 has no dependencies; Q2 depends on Q1. Chain identification. Total chains = 1; Chain 1 indices = [0,1]; Questions = Q1 → Q2.
Appendix
Table 28
End-to-End Retrieval Trace (Step 2.2/3): Retrieval Hop 2 with Beam Search
Hop 2/2 (Q2): “What is the place of birth of <ENTITY_Q1> ?” State. Prior entities = {0: Nicki Minaj}; Active beams = 5; Concrete questions = 5 (Nicki Minaj; Lil Wayne; Changed It; No Frauds; Regret in Your Tears). Top retrieved entities (BM25 + Embedding; top-5 of 20): Rank Entity name 1 Nicki Minaj 2 Nicki Minaj 3 Pink Friday 4 Nicki Minaj 5 Nicki Minaj Retrieved QA pairs after reranking (top-5 of 15; remaining omitted): Rank Question Answer Source 1 Where was Nicki Minaj born? Saint James, Port of Spain Nicki Minaj 2 Who was born in Saint James, Port of Spain? Nicki Minaj Nicki Minaj 3 Who was raised in Queens, New York City? Nicki Minaj Nicki Minaj 4 Where was Nicki Minaj raised? Queens, New York City Nicki Minaj 5 Who performed “Changed It”? Nicki Minaj, Lil Wayne Nicki Minaj Beam expansion (final hop). Use all QA pairs directly (no grouping); candidates = 15. Sample candidate states (first 5): Cand. last_hop Answer entity/entities 1 0.8945 [Saint James, Port of Spain] 2 0.7891 [Nicki Minaj] 3 0.6836 [Nicki Minaj] 4 0.6289 [Queens, New York City] 5 0.5898 [Nicki Minaj, Lil Wayne] Beam pruning (Hop 2). Total candidates = 15; keep top-5 by (chain_score,last_hop_score) . Beam details (Hop 2). Includes all kept beams plus the top-ranked pruned beam. Beam chain_score last_hop Status Entity map 1 0.9315 0.8945 ✓ kept {0: Nicki Minaj, 1: [Saint James, Port of Spain]} 2 0.9052 0.7891 ✓ kept {0: Nicki Minaj, 1: [Nicki Minaj]} 3 0.8781 0.6836 ✓ kept {0: Nicki Minaj, 1: [Nicki Minaj]} 4 0.8637 0.6289 ✓ kept {0: Nicki Minaj, 1: [Queens, New York City]} 5 0.8533 0.5898 ✓ kept {0: Nicki Minaj, 1: [Nicki Minaj, Lil Wayne]} 6 0.8406 0.5430 × pruned {0: Nicki Minaj, 1: [Onika Tanya Maraj-Petty]}
Appendix
Figure 9: Example end-to-end retrieval trace. Panini decomposes the query into two hops and performs chain-following retrieval with beam search (beam width =5 ). At each hop, candidates are scored using geometric-mean (cumulative) chain scoring, and the final answer is generated from the selected QA evidence.
Figure 10: Continual-learning under corpus growth (MuSiQue, 200 questions). We fix a held-out set of 200 questions and evaluate retrieval+QA F1 as the corpus is incrementally expanded from 4K to the full MuSiQue collection ( ∼ 12K passages). The set of relevant passages for these questions is contained in the initial 4K subset and remains unchanged across steps; only the number of distractor passages increases as additional (irrelevant) documents are added. This simulates a continuously evolving corpus where the signal stays constant but the retrieval search space grows.
Retrieval-augmented generation (RAG) has become a standard approach for knowledge-intensive LLM tasks. Complex reasoning remains challenging because queries often require composing dispersed evidence through multiple dependent reasoning steps. Yet existing RAG systems process queries independently, repeatedly retrieving overlapping evidence and reconstructing established reasoning. Preserving such reasoning makes previously retrieved evidence and intermediate results reusable for subsequent queries. However, inference-time reuse becomes increasingly costly as the history grows, motivating a compact representation that preserves useful information without substantial loss in quality. To address these challenges, we present AutoPrunedRetriever (APR), a graph-based RAG system that encodes corpus facts and query-derived reasoning in a shared symbolic codebook. The codebook serves both as the retrieval index and as persistent memory, representing facts, questions, and answers as compact edge sequences, so that the previously established structure can be directly retrieved and reused. At inference time, APR retrieves query-specific reasoning paths and constructs compact prompts, while incremental consolidation keeps the growing memory compact. For deployments where the retriever cannot be replaced, we introduce AutoPruned Layer (APL), which leaves host retrieval unchanged but replaces the retrieved context with the corresponding entries in the shared symbolic codebook, writing each question and answer back as a compact structure. Across five complex-reasoning benchmarks, APR improves accuracy over the strongest baseline by 1.8-10.8 points while using up to 96% fewer input tokens. APL reduces generator input by 60-96% across host systems and preserves answer quality on document-style contexts.
Ning Wang, Kuanyan Zhu, Daniel Yuehwoon Yee +4
Cornell University · University of Cambridge · The University of Hong Kong +2
Retrieval-Augmented Generation (RAG) grounds large language models with external evidence, but many implementations rely on pre-built indices that remain static after construction. Related queries therefore repeat similar multi-hop traversal, increasing latency and compute. Motivated by schema-based learning in cognitive neuroscience, we propose GAM-RAG, a training-free framework that accumulates retrieval experience from recurring or related queries and updates retrieval memory over time. GAM-RAG builds a lightweight, relation-free hierarchical index whose links capture potential co-occurrence rather than fixed semantic relations. During inference, successful retrieval episodes provide sentence-level feedback, updating sentence memories so evidence useful for similar reasoning types becomes easier to activate later. To balance stability and adaptability under noisy feedback, we introduce an uncertainty-aware, Kalman-inspired gain rule that jointly updates memory states and perplexity-based uncertainty estimates. It applies fast updates for reliable novel signals and conservative refinement for stable or noisy memories. We provide a theoretical analysis of the update dynamics, and empirically show that GAM-RAG improves average performance by 3.95% over the strongest baseline and by 8.19% with 5-turn memory, while reducing inference cost by 61%. Our code and datasets are available at: https://anonymous.4open.science/r/GAM_RAG-2EF6.
Sequential memory agents process long documents by reading chunks one after another while maintaining a compact memory state, coupling document traversal to reasoning depth. This coupling introduces sensitivity to evidence placement and ties inference latency linearly to document length. We introduce PARSER, which decouples reading from reasoning. A bank of lightweight subagents each bound to a single chunk read the entire document in parallel, while a lead agent reasons in depth through iterative scatter--gather rounds: at each round it broadcasts a query to all subagents, aggregates the returned evidence, and formulates a deeper follow-up query conditioned on what has been found so far. This decoupled design concentrates all learnable behavior in the lead agent, which is optimized with reinforcement learning, while the subagents remain frozen off-the-shelf models. On multi-hop QA with contexts ranging from 7K to 896K tokens, PARSER with a 4B backbone outperforms the strongest sequential memory baseline by 5.7 points on average and by 12.0 points at 896K tokens. Scaling to a 9B backbone, PARSER surpasses DeepSeek-V4-Pro by 6.3 points. Controlled experiments confirm that PARSER is robust to perturbations in evidence position, order, and distance, conditions that cause large accuracy swings in sequential methods, while reducing inference latency by up to 11x.