Questions about an agent's history often require aggregating events, which retrieval alone does not resolve. We combine source-preserving evidence access with executable aggregation. Trajectory Memory (TM) retains the original trajectory and constructs a source-addressable reading view at question time. TallyMem augments it with a reusable event ledger, model-generated event specifications and deterministic execution. With a commit-specific adaptation on 776 questions from 99 held-out MINTEval repositories, TallyMem raises Qwen3-32B exact match from 35.2% to 49.0%, a 13.8-point gain over TM; gains span 12.8-13.9 points across three readers. A controlled probe with event sets parsed independently from raw logs shows an 11.0-point gain, from 82.8% to 93.8%, without routing. On full AMA-Bench, the routed system yields a smaller 1.3-point gain, from 69.8% to 71.1%. These results support executable aggregation on tasks that require combining events, with benefits dependent on the task and event specification, the largest residual system-error category in our diagnostic analysis.
Figures & tables
Figure 1: Evidence access and event aggregation on an action-frequency question. Left: an illustrative compression example, not a measured output. Middle: Qwen3-32B’s actual long-context answer miscounts five of eight action types. Right: with TallyMem’s group-count query over all 150 steps, Qwen3-32B reports every count correctly. Red numbers are wrong.
Dimension
Binder
ChatDB
AMA-Agent
TM + TallyMem (ours)
Memory
Task input used by generated programs
SQL databases as symbolic memory
Structured trajectory memory with causal links
Original archive + reusable event ledger
Execution
Programs with language-model API calls
Model-generated SQL instructions
Trajectory-based code when retrieval is insufficient
Explicit event specification + fixed executor
Evidence use
Program execution over task input
Database operations and their results
Memory retrieval with code-based processing
Aggregates and step IDs alongside original evidence
Study focus
Program-based question answering
Symbolic memory for multi-hop reasoning
Long-horizon trajectory memory
Access, event selection and aggregation examined separately
Table 1: Design comparison with executable-query and memory systems ( Cheng et al., 2022 ; Hu et al., 2023 ; Zhao et al., 2026 ) . Entries summarize published designs and study objectives; bold highlights our archive–ledger organization and event-level analysis.
Figure 2: Compiler-defined load diagnostic and log-defined aggregation probe. Left: TM (Qwen3-32B) on the 804 AMA-Bench questions with query evidence, by the steps their compiled queries match (406, 186, 108, 64 and 40 questions). Right: the probe, by the size of the event set parsed from the raw log (192, 157, 147 and 138 questions); the fixed counter groups steps by tool, and TM’s curve includes its counter on “how many times” questions.
Figure 3: Two constraints on trajectory answering. Qwen3-32B on 438 rule-selected diagnostic questions with a measured load, with computed evidence always attached. “History fits” means at most 24k raw-history tokens, not full visibility of every TM field. Cells show accuracy (%), sample size and paired sign-test p .
Figure 4: TM and TallyMem through a shared running example. (a) TM retrieves source text from archive A ; TallyMem adds computed evidence to the same reader. (b) The AMA ledger combines program-derived fields with model annotations before questions arrive. The outcome check verifies quote occurrence, not semantic correctness; MINTEval uses a programmatic ledger. (c) The model specifies scope, event predicate and operation; the executor selects and groups all 150 rows. The seven circles are the open group; its count and step IDs enter Dq .
System
Qwen3-32B
gpt-oss-120b
Qwen3-8B
AMA-Bench: accuracy (%), 2,496 questions
Long context
65.0
73.6
57.6
BM25
46.1
–
–
Dense RAG
36.5
–
–
Rolling summary
36.1
–
–
Mem0
24.3
–
–
Table 2: Main results. Each column compares systems within one setting; external memory systems run in the same deployment with the Qwen3-32B reader. MINTEval uses 99 held-out repositories; its long-context row and its gpt-oss-120b and Qwen3-8B entries were run after the pre-registered test. Paired tests and configurations appear in Appendices D and M .
Category
Errors
Other readers (%)
Specification
51
18.1
Ledger
21
38.1
Answering
21
39.3
Partial coverage
11
50.0
Reference or judge
42
17.3
Unclear
2
0.0
Table 3: The 148 errors TallyMem makes with computed evidence (Qwen3-32B, 630 diagnostic questions; Appendix N ). Last column: accuracy (%) of the other four readers given the same evidence.
Appendix figures & tables27 assets
Supplementary material from the paper’s appendix.
Appendix
Figure 5: TM’s archive and reading view (illustrative records). Deterministic indexes guide budgeted selection; the displayed spans retain their original order and provenance. Storing every step does not guarantee that every step or field reaches the reader. The keyword-triggered fixed counter is part of TM and remains present in the TallyMem comparison.
Figure 6: Comparison of Qwen3-32B systems on the 2,496 AMA-Bench questions. TallyMem denotes the routed main system; “no routing” is its ablation. External memory systems use the same deployment, with failed memory constructions counted as wrong. Deployment and adaptation details below define the comparison scope.
System
All
A
B
C
D
vs. TM: wins/losses ( p )
Qwen3-32B reader, official judge, local bf16 deployment
No trajectory (question only)
21.9
21.6
28.5
20.2
15.5
+115/ − 1311 ( < 0.001)
Long context
65.0
65.2
72.0
63.4
57.2
+262/ − 381 ( < 0.001)
TM
69.8
72.2
72.8
73.1
55.3
–
BM25 (baseline suite)
46.1
47.9
54.5
42.3
36.2
+172/ − 763 ( < 0.001)
Dense RAG (baseline suite)
36.5
39.0
46.6
30.8
25.6
+165/ − 997 ( < 0.001)
Appendix
Table 4: Full AMA-Bench, 2,496 questions; accuracy (%). Categories: A recall, B causal inference, C state updating, D state abstraction. † Refined compiler: two compile rules added after inspecting the principal compiler’s errors. ‡ Rule routing: post hoc restriction to pre-defined aggregation families. § With the router of Appendix J ; “no routing” rows attach every nonempty computed evidence. External baselines: same deployment and reader; failed memory constructions count as wrong.
Method
Accuracy (%)
Externally reported results
AMA-agent
56.91
Hipporag2
44.05
HybridFocus
62.46
Long context
51.35
Memorag
45.70
Appendix
Table 5: AMA-Bench method comparison with Qwen3-32B. External scores are reported under their respective settings; this study’s scores come from Table 2 .
Figure 7: Five-reader diagnostic comparison, without routing: (a) all 630 common rule-selected questions; (b) the 37 questions whose compiled queries match more than 100 steps. Each method’s frozen contexts and evidence are shared across readers, with a common judge. The high-load subset is small; paired tests and full load strata appear in Tables 7 and 10 .
Method (Qwen3-32B reader)
correct
vs. TM: +/ − ( p )
No trajectory (question only)
95/630
+44/ − 339 ( < 0.001)
Long context
350/630
+61/ − 101 (0.002)
TM
390/630
–
TM + enumerate-then-count prompt
386/630
+43/ − 47 (0.75)
TallyMem-lite (zero-generation ledger)
407/630
+32/ − 15 (0.02)
TallyMem
416/630
+55/ − 29 (0.006)
Appendix
Table 6: Correct answers on the 630 rule-selected questions. † Refined compiler adds two compile rules chosen after inspecting the principal compiler’s errors (Section 8 ).
Answer model
n
Long context
TM
TallyMem-lite
TallyMem
TallyMem vs. TM
Qwen3-32B (thinking)
630
55.6
61.9
64.6
66.0
+55/ − 29 (0.006)
Qwen3-235B-A22B-2507
630
52.4
59.5
64.3
66.8
+78/ − 32 ( < 0.001)
gpt-oss-120b (reasoning)
630
61.1
68.9
70.2
73.0
+54/ − 28 (0.005)
Qwen3.6-27B (thinking) §
630
64.6
68.4
72.7
72.4
+51/ − 26 (0.006)
Qwen3-8B (thinking) §
630
46.3
54.9
60.3
61.1
+61/ − 22 ( < 0.001)
Appendix
Table 7: Five answer models on the same frozen contexts and evidence (accuracy %, paired sign tests; the judge model is Qwen3-32B throughout). § Answered on our local server (bf16); the others on DeepInfra.
Family
TM → TallyMem ( p )
Count / frequency (F1)
152 → 176 / 246 ( < 0.001)
First / last change (F2)
91 → 92 / 152 (1.00)
State tracing (F3)
113 → 115 / 186 (0.83)
Action enumeration (F4)
52 → 72 / 91 ( < 0.001)
Appendix
Table 8: TM → TallyMem correct / questions by rule-defined family on the 630 rule-selected questions ( p from a paired sign test; one question may be in several families).
Answer model
trajectory fits
does not fit
Qwen3-32B (thinking)
66.0 / 69.6 / 75.1 (0.01)
45.5 / 54.5 / 57.3 (0.21)
Qwen3-235B-A22B-2507
62.5 / 68.0 / 73.5 (0.02)
42.7 / 51.4 / 60.4 ( < 0.001)
gpt-oss-120b (reasoning)
75.4 / 80.9 / 82.2 (0.64)
47.4 / 57.3 / 64.2 ( < 0.001)
Qwen3.6-27B (thinking) §
79.6 / 77.3 / 79.9 (0.24)
50.2 / 59.8 / 65.1 (0.01)
Qwen3-8B (thinking) §
56.6 / 58.6 / 65.4 (0.001)
36.4 / 51.4 / 57.0 (0.010)
Appendix
Table 9: Long context / TM / TallyMem accuracy (%) by nominal raw-history fit (630 questions; p : TallyMem vs. TM). When it does not fit, long context collapses (retrieval bottleneck); when it fits, computation adds significantly for three of the five readers. Fit does not guarantee full TM field visibility (Appendix F ). § Local server.
Answer model
load 1–15
load 16–100
load > 100
Qwen3-32B (thinking)
63.2 / 72.9 / 71.2
33.3 / 44.1 / 57.8
2.7 / 2.7 / 48.6
Qwen3-235B-A22B-2507
58.9 / 68.6 / 74.2
33.3 / 48.0 / 57.8
2.7 / 0.0 / 51.4
gpt-oss-120b (reasoning)
61.5 / 75.3 / 76.9
57.8 / 64.7 / 66.7
18.9 / 10.8 / 62.2
Qwen3.6-27B (thinking) §
67.6 / 75.9 / 77.6
60.8 / 57.8 / 61.8
27.0 / 18.9 / 62.2
Qwen3-8B (thinking) §
53.2 / 69.6 / 72.2
24.5 / 25.5 / 40.2
2.7 / 0.0 / 43.2
Appendix
Table 10: Accuracy (%) as long context / TM / TallyMem by aggregation load. Of the 630 diagnostic questions, 438 have defined load; counts are 299 / 102 / 37 for the three strata. § Local server.
Answer model
all
+enum. vs. TM
load > 100
Qwen3-32B (thinking)
61.9 / 61.3 / 66.0 / 65.4
+43/ − 47 (0.75)
2.7 / 2.7 / 48.6 / 56.8
Qwen3-235B-A22B-2507 ∗
59.6 / 61.2 / 66.9 / 65.3
+77/ − 67 (0.45)
0.0 / 24.3 / 51.4 / 62.2
gpt-oss-120b (reasoning)
68.9 / 74.0 / 73.0 / 76.3
+61/ − 29 ( < 0.001)
10.8 / 18.9 / 62.2 / 67.6
Appendix
Table 11: Enumeration controls on 630 diagnostic questions (37 with load above 100). Accuracy columns (%): TM / TM with enumeration / TallyMem / TallyMem with enumeration. The comparison column gives wins/losses and sign-test p for TM with enumeration versus TM. ∗ 629 of 630: one question failed four times with a transport error.
Question
Operation
n
∣S⋆∣
TallyMem
TM
Counter
Long context
Correct plan
Code
E1
count go to (three bounds; “how many times”)
count
90
36
100.0
100.0
100.0
78.9
100.0
95.6
E2
as E1 at the full bound (“how often”)
count
30
62
100.0
46.7
100.0
43.3
100.0
36.7
E3
count open
count
30
5
100.0
96.7
100.0
73.3
100.0
100.0
E4
opens and closes of the most-used receptacle
count
25
4
100.0
100.0
100.0
88.0
100.0
100.0
E5
distinct objects taken
distinct
30
2
100.0
100.0
100.0
86.7
100.0
93.3
E6
last step with a take
last
30
2
100.0
90.0
100.0
93.3
100.0
100.0
Appendix
Table 12: Probe templates and accuracy (%, official judge; Qwen3-32B). E: embodied, S: software. n : questions; ∣S⋆∣ : mean number of logged events in the answer’s event set. Counter: the fixed group-by-tool plan run by TallyMem’s executor. Over all 645 questions: TallyMem 93.8, TM 82.8 (TallyMem +79/ − 8, p< 0.001), counter 89.9 (+37/ − 12, p< 0.001).
Bound and wording
∣S⋆∣
TallyMem
TM
Counter
Long
E1
b1 , “how many times”
16
100.0
100.0
100.0
100.0
E1
b2 , “how many times”
31
100.0
100.0
100.0
80.0
E1
b3 , “how many times”
62
100.0
100.0
100.0
56.7
E2
b3 , “how often”
62
100.0
46.7
100.0
43.3
S1
b1 , “how many times”
13
100.0
100.0
97.2
5.6
S1
b2 , “how many times”
26
100.0
97.2
97.2
2.8
Appendix
Table 13: Same count, three bounds and two wordings (accuracy, %; official judge).
Reader
TallyMem
TM
Counter
Long context
Correct plan
Qwen3-32B
93.8
82.8
89.9
49.3
94.9
Qwen3-8B
83.1
70.1
81.9
54.3
84.3
Appendix
Table 14: Probe accuracy (%, official judge) by reader; all arms use the same evidence.
n
Exact
P
R
F1
Counter F1
Expressible
E1
90
90
100.0
100.0
100.0
100.0
90/90
E2
30
30
100.0
100.0
100.0
100.0
30/30
E3
25
25
100.0
100.0
100.0
100.0
30/30
E4
25
21
85.3
88.0
86.0
50.3
24/25
E5
30
30
100.0
100.0
100.0
100.0
30/30
E6
30
30
100.0
100.0
100.0
100.0
30/30
Appendix
Table 15: Event sets of TallyMem’s evidence against S⋆ (questions with a nonempty S⋆ ; P, R and F1 in %).
Reader
All
Development half
Held-out half
Qwen3-32B
+ 1.3 [ + 0.7, + 2.0]
+ 1.4 [ + 0.6, + 2.2]
+ 1.3 [ + 0.4, + 2.2]
gpt-oss-120b
+ 0.8 [ + 0.2, + 1.4]
+ 0.6 [ − 0.2, + 1.4]
+ 1.1 [ + 0.3, + 2.0]
Qwen3.6-27B
+ 0.7 [ + 0.1, + 1.4]
+ 1.4 [ + 0.5, + 2.3]
+ 0.1 [ − 0.7, + 1.0]
Qwen3-8B
+ 1.5 [ + 0.9, + 2.1]
+ 1.8 [ + 1.0, + 2.7]
+ 1.2 [ + 0.4, + 2.1]
Qwen3-32B (630)
+ 4.1 [ + 1.5, + 6.7]
Qwen3-235B (630)
+ 7.3 [ + 4.0, + 10.6]
Appendix
Table 16: Gain of TallyMem over TM in accuracy points, with 95% intervals from a bootstrap over trajectories. Upper block: the full benchmark (2,496 questions), routed TallyMem. Lower block: the 630 diagnostic questions, TallyMem without routing. Halves follow the trajectory split of Appendix A ; method development used the first.
Reader
none
> 5
> 10
> 15
> 20
> 40
> 100
TM
Qwen3-32B
70.4
70.8
71.0
71.1
71.0
71.0
70.6
69.8
gpt-oss-120b
79.2
79.2
79.3
79.2
79.2
78.9
79.1
78.4
Qwen3.6-27B
76.8
76.7
76.7
76.6
76.6
76.7
76.5
75.9
Qwen3-8B
62.4
61.9
62.2
62.3
62.1
61.9
61.4
60.7
Appendix
Table 17: Full-benchmark accuracy (%) when computed evidence is attached only above a load threshold (“none”: always; TM: never).
Reader
Questions
TM
no routing
TallyMem
Qwen3-32B (server)
all 2,496
69.8
70.4 (+79/ − 63)
71.1 (+47/ − 14)
rule-selected 670
64.9
68.8 (+52/ − 26)
69.7 (+39/ − 7)
other 1,826
71.6
71.0 (+27/ − 37)
71.6 (+8/ − 7)
gpt-oss-120b (DeepInfra, pre-registered)
all 2,496
78.4
79.2 (+75/ − 54)
79.2 (+40/ − 19)
rule-selected 670
70.2
74.0 (+54/ − 28)
73.3 (+33/ − 12)
other 1,826
81.4
81.1 (+21/ − 26)
81.4 (+7/ − 7)
Appendix
Table 18: Accuracy (%) and wins/losses against TM with and without the router on full AMA-Bench.
Variant
correct
vs. TallyMem
vs. TM
TallyMem
416/630
–
+55/ − 29 (0.006)
no quote recovery
415/630
+2/ − 3 (1.00)
+55/ − 30 (0.009)
no relaxed retry
415/630
+2/ − 3 (1.00)
+52/ − 27 (0.007)
zero-generation ledger (TallyMem-lite)
407/630
+28/ − 37 (0.32)
+32/ − 15 (0.02)
plan self-consistency ( k=5 )
414/630
+28/ − 30 (0.90)
+50/ − 26 (0.008)
earlier development controls, 217 questions
Appendix
Table 19: Ablations (Qwen3-32B). Upper block: 630 rule-selected questions. Lower block: earlier development variants compared with their own TM context.
All
Load > 100
Questions
630
37
TM
61.9
2.7
TallyMem
66.0
48.6
Free-form code (ledger + log)
62.1 (+72/ − 47; 0.03)
32.4 (+9/ − 3; 0.15)
Free-form code (ledger only)
61.1 (+77/ − 46; 0.007)
40.5 (+9/ − 6; 0.61)
Free-form code, thinking (ledger + log)
60.2 (+82/ − 45; 0.001)
45.9 (+9/ − 8; 1.00)
Appendix
Table 20: Free-form code control (Qwen3-32B, accuracy %). Columns: the 630 rule-selected questions and those with load above 100. In parentheses: questions that TallyMem answers correctly and the code arm does not / the reverse, and the two-sided sign-test p .
Figure 8: MINTEval value-history questions by commits back to the requested value: 288 questions with an annotated lookback of at least one commit (bin sizes shown). TallyMem exceeds TM in every bin; the two farthest bins also show larger advantages over long-context reading. The bins describe different questions, not a controlled intervention on history length.
System
All
Hist.
Order
Count
Simple
Multi-hop
vs. TM
Long context
39.2
45.8
19.0
52.5
47.1
33.6
+123/ − 92 (0.041)
TM
35.2
41.0
17.6
31.1
47.8
31.3
–
TallyMem ¶
49.0
56.6
43.1
47.5
56.6
31.3
+124/ − 17 ( < 0.001)
TallyMem, no routing
49.0
56.6
43.1
47.5
56.6
31.3
+125/ − 18 ( < 0.001)
TallyMem, AMA load routing §
35.7
–
–
–
–
–
+5/ − 1 (0.22)
Appendix
Table 21: MINTEval GitHub split, 99 held-out repositories (776 questions), exact match (%), Qwen3-32B reader, frozen system evaluated once. Long context: the whole history, head and tail truncated to the budget of the AMA-Bench long-context arm, run after the pre-registered test as a reference row. Sign tests are paired with TM. ¶ With the coverage-aware router of Appendix J . § The load-only router.
Questions
Errors
Specification
Ledger
Answering
Partial
Reference/judge
Unclear
Load 1–15
86
24
13
8
9
30
2
Load 16–100
43
17
8
10
2
6
0
Load > 100
19
10
0
3
0
6
0
Count / frequency (F1)
52
21
14
10
5
2
0
First / last change (F2)
29
10
1
2
3
12
1
State tracing (F3)
55
7
7
8
4
28
1
Appendix
Table 22: Errors with computed evidence by aggregation load and question family (Qwen3-32B, 630 diagnostic questions). A question can belong to several families. Executor and truncation errors: none.
Figure 9: A conceptual example of Equation 12 . The executed set includes an extra event and misses an intended one. Their effects cancel in the scalar count, although the selected sets differ. S⋆ is an analysis reference, not an input to inference. Rendering and final answer generation can introduce additional errors.
Domain
n
Long ctx.
TM
TallyMem-lite
TallyMem
TallyMem vs. TM
Embodied
239
129
141
161
165
+33/ − 9 ( < 0.001)
Game
17
12
10
10
8
+0/ − 2 (0.50)
Open-world
59
30
45
45
43
+0/ − 2 (0.50)
Software
67
27
21
21
20
+3/ − 4 (1.00)
Text-to-SQL
153
97
99
96
107
+16/ − 8 (0.15)
Web
95
55
74
74
73
+3/ − 4 (1.00)
Appendix
Table 23: Correct answers by domain on the 630 rule-selected questions (Qwen3-32B). The gain is concentrated in the embodied domain, whose trajectories are long and whose questions aggregate the most steps. Long context is better than TM on software engineering and games (small samples), where TM’s selection misses context.
Figure 11: Reading and execution on the worked example. Left: TM’s reading view and the full-ledger computation path. Right: saved TM counts and the executable result against the reference.
Figure 12: Saved answers across reader configurations on the same question. Red entries differ from the reference; all six answer rows come from diagnostic runs.
Long-horizon agents often compress interaction histories into write-time summaries. This creates a fundamental write-before-query barrier: compression decisions are made before the system knows what a future query will hinge on. As a result, summaries can cause unverifiable omissions -- decisive constraints (e.g., allergies) may be dropped, leaving the agent unable to justify an answer with traceable evidence. Retaining raw logs restores an authoritative source of truth, but grounding on raw logs by default is expensive: many queries are answerable from summaries, yet raw grounding still requires processing far longer contexts, inflating token consumption and latency. We propose TierMem, a provenance-linked framework that casts retrieval as an inference-time evidence allocation problem. TierMem uses a two-tier memory hierarchy to answer with the cheapest sufficient evidence: it queries a fast summary index by default, and a runtime sufficiency router Escalates to an immutable raw-log store only when summary evidence is insufficient. TierMem then writes back verified findings as new summary units linked to their raw sources. On LoCoMo, TierMem achieves 0.851 accuracy (vs.0.873 raw-only) while reducing input tokens by 54.1% and latency by 60.7%.
Qiming Zhu, Shunian Chen, Rui Yu +2
School of Data Science, The Chinese University of Hong Kong, Shenzhen.
Extraction at ingestion is the wrong primitive for agent memory: content discarded before the query is known cannot be recovered at retrieval time. We propose True Memory, a six-layer architecture that shifts the center of the system from a storage schema to a multi-stage retrieval pipeline operating over events preserved verbatim. The full system runs as a single SQLite file on commodity CPU with no external database, vector index, graph store, or GPU. On LoCoMo (1,540 questions across 10 multi-session conversations), True Memory Pro reaches 93.0% accuracy (3-run mean) against 61.4% for Mem0, 65.4% for Supermemory, approximately 71% for Zep, and 94.5% for EverMemOS under a matched gpt-4.1-mini answer model. On LongMemEval (500 questions), True Memory Pro reaches 87.8% (3-run mean). On BEAM-1M (700 questions at the 1-million-token scale), True Memory Pro reaches 76.6% (3-run mean), above the prior published result of 73.9% for Hindsight. A 56-configuration ablation shows a 1.3-percentage-point spread within the top-performing configuration family.
A language agent's execution history can exceed its context window, requiring its memory system to retrieve complete supporting evidence under a hard token budget. Evidence may span multiple execution events, yet conventional retrievers use fixed token windows and fixed-k metrics that reward individual fragments without showing whether the complete evidence set fits in context. Smaller windows reduce irrelevant text but scatter evidence across candidates, while flat-versus-graph comparisons can conflate candidate design with graph propagation. To address these limitations, we formulate agent-memory retrieval as budgeted evidence completion and score exact gold spans in shared source coordinates. We first construct source-aligned provenance units from tool arguments and outputs. We then apply a zero-initialized residual R-GCN to refine frozen dense-retrieval scores over typed provenance edges. We evaluate 2,000 span-grounded memory queries over 1,207 held-out execution-grounded ISETrace trajectories. With matched Dense-FT scoring, provenance units improve Full Support@2048 by 19.07 points over flat 512-token windows and remain 11.96 points above a per-metric oracle over four flat chunk sizes; the pattern also holds with cross-encoder scoring. Holding the candidates and seed scores fixed, graph propagation adds 4.55 points in Full Support@2048 (95% CI [2.98, 6.18]). This gain is concentrated when gold evidence spans multiple events; entity co-occurrence expansion produces no comparable benefit, and relation and topology controls confirm dependence on typed transformations and observed graph structure. Overall, source-aligned candidates address the dominant granularity trade-off, while graph-conditioned propagation adds a smaller, targeted benefit for distributed evidence.
Yiqi Wang, Jinqian Ju, Jiaqi Zhang +4
University of Southern Queensland, Australia · Southern University of Science and Technology, China · Jiangsu University, China +2