                            IE            MSR                   TR              KU              AR             Overall
  Claude Sonnet 4.5 28.0 26.4 19.5     13.3 9.1    2.8   40.2 35.1 33.0   29.3 21.6 16.4    97.8 94.4 93.3    36.5 32.4 27.8

       Gemini-3.1-Pro 57.3 58.9 55.8   32.2 31.5 29.4    40.9 42.3 41.2   49.1 48.6 46.0    97.8 95.6 96.7    54.1 53.0 52.0

              GPT-5.4 69.5 63.0 60.2   28.2 27.3 21.7    39.2 42.3 39.7   47.4 43.1 43.1    97.8 96.7 94.4    52.7 52.3 49.6

            Kimi-K2.5 54.8 52.4 51.6   44.1 35.7 28.7    53.1 52.1 48.2   50.9 49.1 48.6    97.8 97.8 93.3    54.9 54.0 52.0

Qwen3.5-122B-A10B 74.4 67.1 43.1       30.1 27.3 22.4    51.5 51.5 41.8   49.1 48.6 46.0    88.9 84.4 83.3    58.7 55.9 45.5

         Qwen3.5-27B 70.3 62.2 54.1    29.4 21.0 14.0    39.2 35.6 34.0   42.2 44.0 38.8    86.7 67.8 63.3    53.0 46.1 40.7

Nemotron-Nano-12B 63.8 56.1 —          16.8 21.0   —     44.8 42.8   —    39.7 34.5   —     45.6 35.6   —     45.0 40.9   —

Cosmos-Reason2-8B 55.7 46.3 37.0       23.8 20.3 20.3    48.5 45.9 43.8   31.9 24.1 12.9    68.9 54.4 35.6    46.1 39.2 31.9

      Phi4-Multimodal 31.7 28.0 22.8   20.3 21.0 22.4    43.8 39.2 30.9   19.8 24.1 14.7    43.3 21.1 15.6    32.2 28.1 22.7

            GLM-4.6V 59.3 61.8 52.8    20.3 23.8 23.1    53.1 53.1 43.8   44.0 34.5 28.4    93.3 64.4 30.0    52.3 49.0 39.0

         Gemma3-27B 35.0 26.4 19.1     23.8 22.4 18.2    41.2 44.3 37.1   30.2 27.6 13.8    68.9 61.1 48.9    37.6 34.2 26.0

      Qwen3-VL-235B 51.6 38.6 29.3     25.2 18.2 15.4    49.5 42.8 43.3   36.2 35.3 30.2    88.9 75.6 67.8    48.3 39.7 34.7

        Qwen3-VL-30B 41.5 39.0 33.7    11.9 11.9 10.5    35.6 37.1 36.6   30.2 29.3 20.7    71.1 65.6 60.0    36.4 35.2 31.3

                Mem0 13.1 11.5 8.2     25.7 22.9 20.0    50.0 45.8 50.0   17.2 24.1 24.1    77.3 77.3 77.3    31.8 31.3 30.3

              MemOS 18.0 18.0 16.4     22.9 22.9 22.9    39.6 39.6 43.8   24.1 24.1 17.2    68.2 68.2 81.8    30.3 30.8 31.3

        MemAgent-7B 18.0 13.1 14.8     25.7 28.6 28.6    62.5 45.8 60.4   41.4 24.1 20.7    13.6 4.5    9.1   32.8 25.6 28.2

           Memory-T1 18.0 19.7 21.3    25.7 22.9 22.9    62.5 56.2 56.2   13.8 17.2 27.6    9.1 18.2 13.6     28.7 28.7 30.3

            M3-Agent 18.0 18.0 22.9    22.9 17.1 20.0    29.2 37.5 22.9   6.9   3.5   6.9   13.6 4.5 18.2     19.5 19.0 19.5

                 M2A 14.8 18.0 21.3    8.6 14.3 11.4     2.1 25.0 27.1    0.0   0.0   0.0   22.7 13.6 13.6    15.4 15.9 16.9

                       32K 64K128K     32K 64K128K       32K 64K128K      32K 64K128K       32K 64K128K       32K 64K128K

Figure 2: Per-type accuracy (%) by context length for representative 13 LVLMs and 6 memory-
augmented agents. Each panel shows one question type; cells use a green colormap. Missing
cells indicate models that exceed their usable context budget at 128K. LVLMs are evaluated on the
full 789-question benchmark, agents on the 195-question canonical subset. Full rosters appear in
Tables 13 and 14.


4.2     Main Results

Overall performance. Figure 2 reports per-type accuracy across context lengths for a represen-
tative 13-LVLM cohort and six memory agents; the full 27-LVLM and seven-agent rosters are in
Appendix G.1. At 32K, the top eight LVLMs fall within a 6.34% band, so short-context accuracy
no longer separates frontier systems. The picture inverts at 128K: several open-weight leaders lose
more than 13%, while Gemini-3.1-Pro retains 51.99% accuracy and degrades least overall (a 2.11%
drop). AR shows the steepest context-driven decline in the open-weight LVLM family, suggesting
that hallucination control is the ability most exposed to evidence dilution at long contexts. Memory
agents occupy a narrower range, with the top four text-only systems clustering within 5% of each
other, while M3-Agent, M3C, and M2A fall substantially lower. Memory agents trail LVLMs across
nearly all types, with the largest gaps on visually grounded retrieval (IE, KU) and on answer refusal
(AR).

Type-specific difficulty. Per-type accuracy ceilings range from 97.78% on AR down to 44.06%
on MSR (Kimi-K2.5) at 32K. AR, which serves as a calibration check for hallucination detection,
is the easiest type as expected, although its apparent ease erodes substantially at long contexts
for open-weight LVLMs (e.g., GLM-4.6V AR drops from 93.33% at 32K to 30.00% at 128K).
TR follows (60.82% ceiling), because timestamps and dates in session metadata provide explicit
retrieval anchors. IE (74.39% ceiling) and KU (50.86%) form an intermediate band: IE requires
visual grounding, with Entity questions demanding two-hop reasoning through the abstracted image
reference, while KU requires tracking a four-fact update chain in which missing a single image anchor


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