Long-running work accumulates material: the original goal, later constraints, attempted routes, failed experiments and current progress. Sending everything again on each continuation repeats more and more content; keeping only a summary can make the original basis of a decision difficult to recover.
Long-running work accumulates material: the original goal, later constraints, attempted routes, failed experiments and current progress. Sending everything again on each continuation repeats more and more content; keeping only a summary can make the original basis of a decision difficult to recover.
LoopX's context delivery separates these jobs: preserve complete sources, read the facts needed for the current decision, expand supporting material around questions, and reuse unchanged structures exactly within a valid compaction cycle.
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Results first: progressive reading, deltas and task ablation
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Eight tasks: progressive reading and in-cycle deltas
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This section recalculates the published eight-task Four-Arm Context ON 18h aggregates to distinguish Progressive and in-cycle Delta transport costs. The source reports 213 model-facing wakes: 101 new-session or post-compaction baseline reads and 112 in-cycle continuations. Per-task cells retain the published values; totals are recomputed from those cells. Raw per-wake logs are not included here.
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Progressive reading: across all 213 wakes, full packets fall from 22.41 MB to 7.29 MB (−67.5%). The projection retains 32.5% of original bytes, with a per-task range of 26.5%–39.0%; unselected candidates and historical detail remain available on demand.
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In-cycle deltas: across 112 continuations, corresponding full packets fall from approximately 12.53 MB to 4.49 MB (−64.2%). Deltas retain 35.8% of same-wake full bytes, with a per-task range of 20.7%–47.1%.
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Progressive + delta first read: for those same 112 continuations, the --initial-read body carries 3.0% of original bytes, or 5.0% including the CLI JSON envelope (−97.0% / −95.0%). First-read size is not whole-episode model input.
A: Per-task shares, with Progressive over all wakes and Delta/first-read over in-cycle wakes only. B: Separate cohort totals: Progressive 7.29 / 22.41 MB over 213 wakes; Delta 4.49 / approximately 12.53 MB over 112 wakes. First-read body / CLI totals are 0.374 / 0.631 MB for the same 112 wakes. The two denominators are not interchangeable. Open image for full size.
+Expand the eight-task data table
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+
+
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Task (Four-Arm Context ON 18h)
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Wakes (base / Δ)
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Full packets
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Progressive projection (share)
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In-cycle Delta payload (vs same-wake Full)
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Prog+Delta first-screen (body / CLI)
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+
+
+
+
Grid Robot (on_stride)
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29 (15 / 14)
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4,405,651 B
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1,166,467 B (26.5%, −73.5%)
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474,016 B (20.7%, −79.3%)
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41,878 / 76,650 B (1.8% / 3.3%)
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+
+
VLIW Scheduling (on_stride)
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14 (5 / 9)
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1,297,500 B
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505,826 B (39.0%, −61.0%)
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382,664 B (45.2%, −54.8%)
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30,860 / 50,152 B (3.6% / 5.9%)
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+
+
FFmpeg Encoding (on_stride)
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26 (12 / 14)
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2,924,522 B
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874,104 B (29.9%, −70.1%)
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759,787 B (47.1%, −52.9%)
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43,807 / 75,305 B (2.7% / 4.7%)
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+
+
OpenTTD Transport (on_stride)
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31 (19 / 12)
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3,288,042 B
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1,050,623 B (32.0%, −68.0%)
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436,053 B (30.8%, −69.2%)
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36,753 / 64,619 B (2.6% / 4.6%)
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+
+
Ad Placement (on_stride)
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29 (12 / 17)
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3,115,820 B
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1,019,704 B (32.7%, −67.3%)
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708,828 B (39.4%, −60.6%)
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64,174 / 102,437 B (3.6% / 5.7%)
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+
+
Borden Inversion (on_stride)
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28 (11 / 17)
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2,855,664 B
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1,001,254 B (35.1%, −64.9%)
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625,709 B (34.4%, −65.6%)
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50,602 / 90,024 B (2.8% / 4.9%)
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+
+
Rust Workspace (on_stride)
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15 (4 / 11)
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1,173,832 B
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439,814 B (37.5%, −62.5%)
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363,310 B (42.1%, −57.9%)
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40,956 / 64,251 B (4.7% / 7.4%)
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+
+
ANN Vector Search (on_stride)
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41 (23 / 18)
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3,347,659 B
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1,228,159 B (36.7%, −63.3%)
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737,968 B (38.6%, −61.4%)
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65,419 / 107,112 B (3.4% / 5.6%)
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+
+
8-Run Weighted Total
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213 (101 / 112)
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22,408,690 B (100%)
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7,285,951 B (32.5%, −67.5%)
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4,488,335 B (35.8%, −64.2%)
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374,449 / 630,550 B (3.0% / 5.0%)
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+
+
+
+
+
Basis and source: the published eight task rows sum to 7,285,951 B / 22,408,690 B = 32.5% for Progressive across all 213 wakes. Delta and first-screen shares of approximately 35.8% and 3.0% / 5.0% cover only the 112 in-cycle wakes. Their Full denominator was published rounded as 12.53 MB; it is not the 213-wake total. This is an aggregate recalculation, not a new experiment or an independent audit of per-wake logs. Public aggregate data and provenance.
Implementation status: #8266 remains an open C1 proposal. The final merged scope of #8304 preserves precision while reusing ordering and a verified digest. These cohort bytes do not establish a shipped unified Controller/Worker gain.
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Multi-task and layered ablation: benefit chain and boundary counterexamples
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In EdgeBench ANN vector search optimization (gpt-6.1-sol / xhigh, 18h, subject to Recall@10 ≥ 0.95) and cross-task ablations, we separate Context Delivery (progressive selection and cycle deltas) from Stride (evidence-conditioned experiment sizing), auditing selected control-packet bytes, main-session model input tokens, and local/early-window counterexamples side by side (reconciled from archived trajectories and receipts by @shangzh0):
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Multi-task ablation and layered accounting
Top (A): ANN 18h six-arm peak valid throughput QPS (Recall@10 ≥ 0.95). Bottom (B): Layered control-packet byte and main-session input token savings vs Context off (left, negative) alongside disclosed local/early-window overhead and cross-task counterexamples (right, positive). Open image for full size.
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This dual-track audit shows why control-packet byte reduction (−74.25% / −38.54%) must not be equated with whole-episode model token savings (−12.07% uncached, −7.46% total), nor used to hide early-window indexing overhead (4.05× in the first 2 hours) or cross-task counterexamples. The reported ANN runs contain both lower input totals and later useful experiments. That observation motivates testing whether context delivery helps sustain long research, but does not by itself attribute the outcome to saved attention, separate every interaction with Stride, or establish a gain on another task. The November source changes described later were not treatments in these archived runs. See EdgeBench late-stage breakthroughs and five-arm attribution for the original research context.
+Expand the ablation results and counterexamples
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+
+
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Dimension or experimental arm
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Observed figure
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Scope and interpretation
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ANN 18h: Official / Single / Native-Goal
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5,753 / 5,706 / 4,372 QPS
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Official peaked at 1.05h and remained on a plateau afterward
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+
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ANN 18h: Explore · Context off
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5,766 QPS (+0.2%)
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Peaked at 11.37h; input accounting is reported separately below
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+
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ANN 18h: Explore · Context on
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6,020 QPS (+4.6%)
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Peaked at 14.43h; this run's later peak does not isolate the cause
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+
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ANN 18h: Explore · Context on + Stride
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7,159 QPS (+24.4%)
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Breakthrough at 9.19h (QT_8bit_direct HNSW-SQ8, Recall@10 = 0.9585)
Baseline establishment and staged discovery add upfront work; later amortization is not guaranteed by this early window
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+
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Counterexample 3: Historical Grid (lower is better)
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Official 1967 < Off 2054 < On+Stride 2088 < On 2242
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Cross-task counterexample under differing durations and resources; retained without causal overclaim
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The core native Codex path ships in LoopX 2.2.0. Supported POSIX Codex App/SSH sessions and new benchmark heartbeat trials default to context delivery; saved off remains effective and explicit off is available. When enabled, complete content is saved first and the Agent receives a reading entrypoint for current facts. See the 2.2.0 release notes for publication and validation scope.
Think of a capture as a complete saved snapshot, like a book whose edition is fixed. An index locates its chapters; a JSON Pointer addresses a place inside it. Progressive reading determines how deeply to open the material now. Differential delivery determines what can be reused on the next continuation.
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This November 8 revision also follows the first-read, staged-scope, transport and numeric-fidelity changes merged after 2.2.0, checked at source 611019243e. Those sections describe that source snapshot, not an upgrade already installed everywhere. The original teaching measurements and archived research results below retain their earlier versions and conditions.
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This November 8 revision also follows the first-read, staged-scope, transport and numeric-fidelity changes merged after 2.2.0, checked at source 611019243e. Those sections describe that source snapshot, not an upgrade already installed everywhere. The teaching measurements and opening archived research results retain their earlier versions and conditions.
Follow one task: give an assistant voice input
Suppose an assistant needs to understand a voice note and reply in its original chat. Its complete material contains the goal, provider constraints, privacy requirements, acceptance and a long discussion of alternatives. This is a teaching example; the example program below generates the complete data in an isolated directory.
capture.json
@@ -239,161 +400,8 @@
A shorter view must preserve the original values
What the measurements establish
The example separates three mechanisms: avoid repeatedly moving history not currently needed, combine reads that belong together, and avoid retransmitting unchanged verified structures. Complete sources, requirements and recovery connect them.
Reader figures include actual CLI page metadata; local path length affects a small amount of framing. Projection figures compare capture bodies; the cycle table compares codec output. The reproduction script checks these distinct measurements and exact reconstruction.
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Real long-running tasks must also count necessary later reads, recovery, cache hits, model input/output, tool calls, waiting and human attention. Effectiveness depends on constraints understood, decisions made and accepted outcomes. The synthetic voice example above measures the mechanical path; below we examine multi-task and layered ablation observations on 18-hour trajectories.
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Multi-task and layered ablation: benefit chain and boundary counterexamples
-
In EdgeBench ANN vector search optimization (gpt-6.1-sol / xhigh, 18h, subject to Recall@10 ≥ 0.95) and cross-task ablations, we separate Context Delivery (progressive selection and cycle deltas) from Stride (evidence-conditioned experiment sizing), auditing selected control-packet bytes, main-session model input tokens, and local/early-window counterexamples side by side (reconciled from archived trajectories and receipts by @shangzh0):
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Multi-task ablation and layered accounting
Top (A): ANN 18h six-arm peak valid throughput QPS (Recall@10 ≥ 0.95). Bottom (B): Layered control-packet byte and main-session input token savings vs Context off (left, negative) alongside disclosed local/early-window overhead and cross-task counterexamples (right, positive). Open image for full size.
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-
-
-
Dimension or experimental arm
-
Observed figure
-
Scope and interpretation
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-
-
-
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ANN 18h: Official / Single / Native-Goal
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5,753 / 5,706 / 4,372 QPS
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Official peaked at 1.05h and remained on a plateau afterward
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-
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ANN 18h: Explore · Context off
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5,766 QPS (+0.2%)
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Peaked at 11.37h; input accounting is reported separately below
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-
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ANN 18h: Explore · Context on
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6,020 QPS (+4.6%)
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Peaked at 14.43h; this run's later peak does not isolate the cause
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-
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ANN 18h: Explore · Context on + Stride
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7,159 QPS (+24.4%)
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Breakthrough at 9.19h (QT_8bit_direct HNSW-SQ8, Recall@10 = 0.9585)
Baseline establishment and staged discovery add upfront work; later amortization is not guaranteed by this early window
-
-
-
Counterexample 3: Historical Grid (lower is better)
-
Official 1967 < Off 2054 < On+Stride 2088 < On 2242
-
Cross-task counterexample under differing durations and resources; retained without causal overclaim
-
-
-
-
This dual-track audit shows why control-packet byte reduction (−74.25% / −38.54%) must not be equated with whole-episode model token savings (−12.07% uncached, −7.46% total), nor used to hide early-window indexing overhead (4.05× in the first 2 hours) or cross-task counterexamples. The reported ANN runs contain both lower input totals and later useful experiments. That observation motivates testing whether context delivery helps sustain long research, but does not by itself attribute the outcome to saved attention, separate every interaction with Stride, or establish a gain on another task. The November source changes above were not treatments in these archived runs. See EdgeBench late-stage breakthroughs and five-arm attribution for the original research context.
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2.2.0 ships complete sources, file entrypoints, batch selection, index depth, applicable acceptance layering, exact sharing, cycle deltas and recovery. Broader decision-stage candidate, preference and Explore delivery and consistency across all entrypoints remain unfinished. Disable in Settings → This machine → Capabilities → Differential context → Codex; saved off survives upgrade. A command or new benchmark trial can use --context-delivery off. Trials do not inherit the machine switch, and existing frozen trials are not rewritten. Under the default policy, ordinary CLI, unsupported hosts and non-heartbeat benchmarks retain their full paths; an explicit unsupported on request is rejected. Unavailable hooks use full delivery. Saving configuration, actual installation and model adoption remain separate checks.
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Multi-task control-plane byte ratios across 8 latest Four-Arm Context ON 18h trajectories (Progressive vs. Delta)
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This section recalculates the published eight-task Four-Arm Context ON 18h aggregates to distinguish Progressive and in-cycle Delta transport costs. The source reports 213 model-facing wakes: 101 new-session or post-compaction baseline reads and 112 in-cycle continuations. Per-task cells retain the published values; totals are recomputed from those cells. Raw per-wake logs are not included here.
A: Per-task shares, with Progressive over all wakes and Delta/first-read over in-cycle wakes only. B: Separate cohort totals: Progressive 7.29 / 22.41 MB over 213 wakes; Delta 4.49 / approximately 12.53 MB over 112 wakes. First-read body / CLI totals are 0.374 / 0.631 MB for the same 112 wakes. The two denominators are not interchangeable. Open image for full size.
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-
-
-
Task (Four-Arm Context ON 18h)
-
Wakes (base / Δ)
-
Full packets
-
Progressive projection (share)
-
In-cycle Delta payload (vs same-wake Full)
-
Prog+Delta first-screen (body / CLI)
-
-
-
-
-
Grid Robot (on_stride)
-
29 (15 / 14)
-
4,405,651 B
-
1,166,467 B (26.5%, −73.5%)
-
474,016 B (20.7%, −79.3%)
-
41,878 / 76,650 B (1.8% / 3.3%)
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-
-
VLIW Scheduling (on_stride)
-
14 (5 / 9)
-
1,297,500 B
-
505,826 B (39.0%, −61.0%)
-
382,664 B (45.2%, −54.8%)
-
30,860 / 50,152 B (3.6% / 5.9%)
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-
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FFmpeg Encoding (on_stride)
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26 (12 / 14)
-
2,924,522 B
-
874,104 B (29.9%, −70.1%)
-
759,787 B (47.1%, −52.9%)
-
43,807 / 75,305 B (2.7% / 4.7%)
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-
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OpenTTD Transport (on_stride)
-
31 (19 / 12)
-
3,288,042 B
-
1,050,623 B (32.0%, −68.0%)
-
436,053 B (30.8%, −69.2%)
-
36,753 / 64,619 B (2.6% / 4.6%)
-
-
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Ad Placement (on_stride)
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29 (12 / 17)
-
3,115,820 B
-
1,019,704 B (32.7%, −67.3%)
-
708,828 B (39.4%, −60.6%)
-
64,174 / 102,437 B (3.6% / 5.7%)
-
-
-
Borden Inversion (on_stride)
-
28 (11 / 17)
-
2,855,664 B
-
1,001,254 B (35.1%, −64.9%)
-
625,709 B (34.4%, −65.6%)
-
50,602 / 90,024 B (2.8% / 4.9%)
-
-
-
Rust Workspace (on_stride)
-
15 (4 / 11)
-
1,173,832 B
-
439,814 B (37.5%, −62.5%)
-
363,310 B (42.1%, −57.9%)
-
40,956 / 64,251 B (4.7% / 7.4%)
-
-
-
ANN Vector Search (on_stride)
-
41 (23 / 18)
-
3,347,659 B
-
1,228,159 B (36.7%, −63.3%)
-
737,968 B (38.6%, −61.4%)
-
65,419 / 107,112 B (3.4% / 5.6%)
-
-
-
8-Run Weighted Total
-
213 (101 / 112)
-
22,408,690 B (100%)
-
7,285,951 B (32.5%, −67.5%)
-
4,488,335 B (35.8%, −64.2%)
-
374,449 / 630,550 B (3.0% / 5.0%)
-
-
-
-
Two orthogonal byte-reduction stages appear consistently across all 8 tasks: (1) Progressive projection (required-context.json) reduces full decision packets across all 213 wakes from 22.41 MB to 7.29 MB (32.5% weighted byte share, −67.5%, per-task range 26.5%–39.0%) by folding unselected candidates and historical long-tail detail behind on-demand JSON Pointers; (2) in-cycle Delta delivery (delivery.json) reduces the 112 in-cycle wakes from 12.53 MB to 4.49 MB (35.8% weighted byte share, −64.2%, per-task range 20.7%–47.1%); and (3) their combined --initial-read first-screen entrypoint carries only 3.0% of raw bytes (5.0% including the CLI JSON reader envelope, i.e., −97.0% / −95.0%).
+
Real long-running tasks must also count necessary later reads, recovery, cache hits, model input/output, tool calls, waiting and human attention. Effectiveness depends on constraints understood, decisions made and accepted outcomes. The synthetic voice example above measures the mechanical path; the opening eight-task comparison and 18-hour ablations separately examine transport cost, model input and task outcomes in long-running research.
2.2.0 ships complete sources, file entrypoints, batch selection, index depth, applicable acceptance layering, exact sharing, cycle deltas and recovery. Broader decision-stage candidate, preference and Explore delivery and consistency across all entrypoints remain unfinished. Disable in Settings → This machine → Capabilities → Differential context → Codex; saved off survives upgrade. A command or new benchmark trial can use --context-delivery off. Trials do not inherit the machine switch, and existing frozen trials are not rewritten. Under the default policy, ordinary CLI, unsupported hosts and non-heartbeat benchmarks retain their full paths; an explicit unsupported on request is rejected. Unavailable hooks use full delivery. Saving configuration, actual installation and model adoption remain separate checks.
-
-
Basis and source: the published eight task rows sum to 7,285,951 B / 22,408,690 B = 32.5% for Progressive across all 213 wakes. Delta and first-screen shares of approximately 35.8% and 3.0% / 5.0% cover only the 112 in-cycle wakes. Their Full denominator was published rounded as 12.53 MB; it is not the 213-wake total. This is an aggregate recalculation, not a new experiment or an independent audit of per-wake logs. Public aggregate data and provenance.
Implementation status: #8266 remains an open C1 proposal. The final merged scope of #8304 preserves precision while reusing ordering and a verified digest. These cohort bytes do not establish a shipped unified Controller/Worker gain.
Read the same material yourself
example.py generates this complete synthetic voice task and invokes the existing reader, progressive projection and delta owners. It checks selected values, directory addresses, all ten requirements, shared references and complete recovery. It registers no Goal, admits or settles no Turn, and calls no model or speech service.
Prepare the original measurement checkout, a4a0fad889d91cba0d945ce658788bdb76ba351e, and its compatible dependencies. Run from that checkout’s root, substituting the downloaded program path. The output directory must be empty.
