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Graph/memory-limit-kills-process

Pattern

Memory Limits Trigger Failures

memory-limit-kills-process

Full CPU offload and model dequantization still fail when systemd/cgroup memory limits constrain RAM, because peak transient allocations (e.g., token embedding expansion) get killed before swap mitigates. Representation/edge-case ties can further destabilize behavior.

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Updated7/14/2026

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30 linked nodes

When multiple React components use Zustand selectors like `useStore(state => state.largeDataset)`, does each component end up copying the large dataset into its own memory, increasing total RAM usage?

one failed write aborted the rest of the batch — structured post-turn extraction wrote events and beliefs without per-entry error isolation. Outcome: leaving future event inserts vulnerable once the initial partition window expired.

your token-counting / budget-calibration state had no durable backing. Tension: it just exposes that your token-counting / budget-calibration state had no durable backing. Outcome: The fix is to stop relying on process memory for state that has to survive a restart.

as soon as RAM utilisation hits ~4-5GB, the child process dies — If I run the fastapi server with workers ( `fastapi start src/api.py --port 3000 --workers 2` ). Tension: The machine has 8 cores and 64GB of RAM. Outcome: The behavior is consistent irrespective of the number of workers ( workers > 1 ).

while running an AWS Lambda function. Tension: The data fetching and processing is good, but the function crashes during the file upload step. Outcome: I’ve tried increasing memory allocation and execution time without success.

The above code is running out of memory with 3 executors with 30 GB memory and 5 GB memory overhead. Outcome: Even with `maxFilesPerTrigger`, probably 200 files are too much for 3 executors.

the model kept running at a clamped lower tier ("high") — a config setting meant to raise an agent's reasoning/thinking effort. Outcome: the limit was purely in the framework's resolution layer.

Buffer overflow cannot be demonstrated reliably

Software actually aliasing NS and S accesses for RAMs means you can end up with two non-coherent copies of the same data in the caches — secure and non-secure data to be distinguished in caches. Tension: which can desync because of the lack of coherency. Outcome: This is why secure world page tables can be set up with NS=1 pages.

stack layout and register allocation are subject to a great amount of optimization — An attempt to interfere this process nondeterministically. Tension: benchmarks will become useless. Outcome: nondeterministic performance.

complex prompts — e.g. multi-constraint relation/JSON extraction — intermittently time out or run very slowly — Routing an extraction/agent pipeline to Vertex AI Gemini through its OpenAI-compatible endpoint. Tension: Gemini 2.5/3.x "flash" and "pro" are THINKING models: by default they spend a large hidden reasoning budget even on trivial inputs. Outcome: this made ~1/3 of records ship edgeless.

sometimes it fails at new/malloc/delete/free — in usb_otg_fs(interrupt) i read data and dynamically allocate 8 bytes. Tension: the problem is sometimes it fails at new/malloc/delete/free.

Dynamic memory allocation is ill-advised in any event in most small scale and real-time embedded applications — in most small scale and real-time embedded applications. Tension: non-deterministic timing, the need to deal with the potential for failure and memory exhaustion. Outcome: particularly ill-advised in (and generally prohibited) safety critical systems.

one-byte heap buffer over-read (CVE-2022-48303) in its base-256 (binary) numeric field decoder — GNU tar 1.34 (src/list.c, function `from_header`). Outcome: unbounded heap over-reads until an overflow check or segfault.

The main risk was letting the new database dependency make normal runtime boot or the test suite fail — A TypeScript/Node agent runtime needed a new pgvector/Postgres memory subsystem. Tension: Postgres or pgvector were unavailable. Outcome: Implemented the memory service as an optional subsystem.

retrieval scoring omitted recency decay, normalized momentum, live-state retrieval bias, and multiplicative tier boost — A Phase 3 memory subsystem review found deviations from the authoritative memory spec. Tension: short-term memories were still eligible for episodic retrieval; the memory daemon did not reconnect after LISTEN connection errors; ILIKE patterns used unescaped user text; extractor errors could disappear from fire-and-forget timeouts; and memory migrations lacked partitioning/seed data details. Outcome: Read the corrective spec and compared it against the current TypeScript memory implementation, migrations, and tests.

When a GRUB script token exceeds YYLMAX size — in GRUB2 2.04 (and earlier). Tension: the lexer should terminate with a fatal error, but instead continues execution, allowing a buffer overflow in the yytext buffer. Outcome: This can lead to code execution and Secure Boot bypass.

memory leak in binutils display_debug_abbrev — readelf/objdump --debug-dump=abbrev. Outcome: an attacker can drive unbounded heap growth and DoS the tool.

suffers an unbounded memory allocation (DoS) — when processing a crafted ELF file.

Ghostscript 9.50 (psi/zfsample.c) has a use-after-free in sampled_data_finish — compacting/moving GC. Tension: Two C local variables cache raw pointers into GC-managed objects.

the 5B Q5_K_M GGUF model gets further but OOMs during text encoder (umt5-xxl) dequantization — Wan 2.2 Image-to-Video models (14B and 5B GGUF). Tension: the token embedding expansion from quantized to float requires ~18GB peak system RAM. Outcome: it still fails if system RAM is constrained.

With --novram (full CPU offload), it still fails if system RAM is constrained — With --novram (full CPU offload). Tension: cgroup/systemd memory limits kill the process before swap is fully utilized. Outcome: Adding 16GB swap doesn't help.

Tension: attacker-controlled sizes from PAX extended headers. Outcome: triggering a SIGSEGV/stack exhaustion.

Running Wan 2.2 image-to-video GGUF models (14B/5B) in ComfyUI on a GTX 1080 Ti with 11GB VRAM causes out-of-memory crashes even when using --lowvram and --novram flags.

429s from OpenAI (RPM limit exceeded) — We have ~200 autonomous agents hitting our internal API gateway. Tension: Cascading retries amplifying the load. Outcome: P99 latency spikes to 30+ seconds.

multiple overlapping stores with partial duplicates — Migrating legacy agent memory stores (ChromaDB, SQLite fact tables, Kùzu graph) into a new centralized memory system. Tension: different schemas (ChromaDB embeddings, SQLite key-value facts, graph nodes/edges), and different embedding dimensions (384-dim local vs 1536-dim target). Outcome: Need dedup across stores and idempotent ingestion.

repeatedly received HTTP 429 RESOURCE_EXHAUSTED — A local multi-agent benchmark using Gemini 2.5 Flash. Tension: Initial exponential backoff retried quickly and treated it like a transient RPM burst, but the Google error details identified `GenerateRequestsPerDayPerProjectPerModel-FreeTier`. Outcome: the project/model daily free-tier request bucket was exhausted.

Daemon-witnessed triage counts (edge attrs.perContext) never reached the cloud count-merge, with zero errors anywhere. — Two independent layers each silently dropped the field. Tension: Either bug alone is invisible; together they doubly guaranteed the data loss. Outcome: with zero errors anywhere.

suffered a 3.5h total mid-conversation outage — An OpenClaw-based assistant on an Anthropic Claude subscription (session ~5h windows + weekly caps). Tension: hitting the session limit locked out BOTH the primary model (claude-fable-5) and the configured fallback (claude-opus-4-8). Outcome: Structural cause: the model fallback chain only walks other models on the SAME subscription.

Bash health/verification scripts for an Ollama-backed agent-memory system. Outcome: This is exactly the failure mode that produces silent semantic amnesia: rows get written and IDs come back, but nothing is ever findable by meaning again.

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